From d376fd6fe0f4c0cea57bf879cc4c2745afbeb52d Mon Sep 17 00:00:00 2001 From: Sevy Harris Date: Tue, 7 Apr 2026 17:15:18 -0400 Subject: [PATCH 1/2] Add notebook demonstrating cov-dep thermo in RMG This notebook demonstrates how to run RMG using a library with coverage-dependent thermo entries. Then it shows a simulation of the resulting mechanism and compares it to the baseline of no coverage dependence. --- ipython/coverage_dependent_thermo.ipynb | 3671 +++++++++++++++++++++++ 1 file changed, 3671 insertions(+) create mode 100644 ipython/coverage_dependent_thermo.ipynb diff --git a/ipython/coverage_dependent_thermo.ipynb b/ipython/coverage_dependent_thermo.ipynb new file mode 100644 index 00000000000..7ceb64b41cb --- /dev/null +++ b/ipython/coverage_dependent_thermo.ipynb @@ -0,0 +1,3671 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "2635da2c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import subprocess\n", + "import cantera as ct\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "markdown", + "id": "e510eda3", + "metadata": {}, + "source": [ + "# Run baseline and coverage-dependent RMG jobs\n", + "\n", + "This should take less than 5 minutes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4daa7653", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running RMG with input file at temp/cov_dep_rmg_run/input.py...\n", + "Global RMG Settings:\n", + " database.directory = /home/moon/rmg/RMG-database/input (Default, relative to RMG-Py source code)\n", + " test_data.directory = /home/moon/rmg/RMG-Py/test/rmgpy/test_data (Default, relative to RMG-Py source code)\n", + "RMG execution initiated at Tue Apr 7 16:44:57 2026\n", + "\n", + "#########################################################\n", + "# RMG-Py - Reaction Mechanism Generator in Python #\n", + "# Version: 3.3.0 #\n", + "# Authors: RMG Developers (rmg_dev@mit.edu) #\n", + "# P.I.s: William H. Green (whgreen@mit.edu) #\n", + "# Richard H. West (r.west@neu.edu) #\n", + "# Website: http://reactionmechanismgenerator.github.io/ #\n", + "#########################################################\n", + "\n", + "The current git HEAD for RMG-Py is:\n", + "\tb'5bbd2707ea570c5a4fb681f0baa071fb1058ad24'\n", + "\tb'Mon Apr 6 15:20:46 2026 -0400'\n", + "\n", + "The current git HEAD for RMG-database is:\n", + "\tb'edfb0e5d715146fd0edd2201a0749217781af2b3'\n", + "\tb'Tue Apr 7 16:09:33 2026 -0400'\n", + "\n", + "Reading input file \"/home/moon/rmg/RMG-Py/ipython/temp/cov_dep_rmg_run/input.py\"...\n", + "\n", + "# Data sources\n", + "database(\n", + " thermoLibraries=['covDepSurfaceThermoPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", + " reactionLibraries = [\n", + " ('Surface/Methane/Deutschmann_Pt', False),\n", + " ('Surface/Methane/Vlachos_Pt111', False),\n", + " 'BurkeH2O2inArHe'],\n", + " seedMechanisms = [],\n", + " kineticsDepositories = ['training'],\n", + " kineticsFamilies =['surface','default'],\n", + " kineticsEstimator = 'rate rules',\n", + ")\n", + "\n", + "catalystProperties( # default values for Pt(111)\n", + " metal = 'Pt111',\n", + " thermoCoverageDependence = True,\n", + ")\n", + "\n", + "# List of species\n", + "species(\n", + " label='X',\n", + " reactive=True,\n", + " structure=adjacencyList(\"1 X u0\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH4',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH4]\"),\n", + ")\n", + "species(\n", + " label='O2',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u1 p2 c0 {2,S}\n", + " 2 O u1 p2 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='Ar',\n", + " reactive=False,\n", + " structure=SMILES(\"[Ar]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CO2',\n", + " reactive=True,\n", + " structure=SMILES(\"O=C=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='H2O',\n", + " reactive=True,\n", + " structure=SMILES(\"O\"),\n", + ")\n", + "\n", + "species(\n", + " label='H2',\n", + " reactive=True,\n", + " structure=SMILES(\"[H][H]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CO',\n", + " reactive=True,\n", + " structure=SMILES(\"[C-]#[O+]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H6',\n", + " reactive=True,\n", + " structure=SMILES(\"CC\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH2O',\n", + " reactive=True,\n", + " structure=SMILES(\"C=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH3]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C3H8',\n", + " reactive=True,\n", + " structure=SMILES(\"CCC\"),\n", + ")\n", + "\n", + "species(\n", + " label='H',\n", + " reactive=True,\n", + " structure=SMILES(\"[H]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H5',\n", + " reactive=True,\n", + " structure=SMILES(\"C[CH2]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3OH',\n", + " reactive=True,\n", + " structure=SMILES(\"CO\"),\n", + ")\n", + "\n", + "species(\n", + " label='HCO',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH]=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3CHO',\n", + " reactive=True,\n", + " structure=SMILES(\"CC=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='OH',\n", + " reactive=True,\n", + " structure=SMILES(\"[OH]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H4',\n", + " reactive=True,\n", + " structure=SMILES(\"C=C\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3CH',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH]C\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3OO',\n", + " reactive=True,\n", + " structure=SMILES(\"CO[O]\"),\n", + ")\n", + "\n", + "species(\n", + " label='HX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 H u0 p0 c0 {2,S}\n", + " 2 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CO2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {3,D}\n", + " 2 O u0 p2 c0 {3,D}\n", + " 3 C u0 p0 c0 {1,D} {2,D}\n", + " 4 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='COX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 C u0 p0 c0 {1,D} {3,D}\n", + " 3 X u0 p0 c0 {2,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH4X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 H u0 p0 c0 {1,S}\n", + " 5 H u0 p0 c0 {1,S}\n", + " 6 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='OX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 X u0 p0 c0 {1,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,D}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 X u0 p0 c0 {1,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH3X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 H u0 p0 c0 {1,S}\n", + " 5 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CHX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,T}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0 {1,T}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,Q}\n", + " 2 X u0 p0 c0 {1,Q}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='H2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 H u0 p0 c0 {2,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='OHX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,S} {3,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='H2OX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,S} {3,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CHOX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 C u0 p0 c0 {1,D} {3,S} {4,S}\n", + " 3 H u0 p0 c0 {2,S}\n", + " 4 X u0 p0 c0 {2,S}\n", + " '''),\n", + ")\n", + "\n", + "surfaceReactor(\n", + " temperature=(600,'K'),\n", + " initialPressure=(1.0, 'bar'),\n", + " initialGasMoleFractions={\n", + " \"CH4\": 0.108574,\n", + " \"O2\": 0.02088,\n", + " \"Ar\": 0.78547,\n", + " },\n", + " initialSurfaceCoverages={\n", + " \"X\": 1.0,\n", + " },\n", + " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", + " terminationConversion = { \"CH4\":0.95,},\n", + " terminationTime=(10., 's'),\n", + " # terminationConversion={'O2': 0.99,},\n", + " terminationRateRatio=0.05\n", + ")\n", + "\n", + "surfaceReactor(\n", + " temperature=(2000,'K'),\n", + " initialPressure=(1.0, 'bar'),\n", + " initialGasMoleFractions={\n", + " \"CH4\": 0.041866,\n", + " \"O2\": 0.03488,\n", + " \"Ar\": 0.131246,\n", + " },\n", + " initialSurfaceCoverages={\n", + " \"X\": 1.0,\n", + " },\n", + " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", + " terminationConversion = { \"CH4\":0.95,},\n", + " terminationTime=(10., 's'),\n", + " # terminationConversion={'O2': 0.99,},\n", + " terminationRateRatio=0.05\n", + ")\n", + "\n", + "\n", + "simulator(\n", + " atol=1e-18,\n", + " rtol=1e-12,\n", + ")\n", + "\n", + "model(\n", + " toleranceKeepInEdge=0.0,\n", + " toleranceMoveToCore=1e-1,\n", + "# inturrupt tolerance was 0.1 wout pruning, 1e8 w pruning on\n", + " toleranceInterruptSimulation=1e8,\n", + " maximumEdgeSpecies=500000,\n", + " maxNumSpecies=44,\n", + "# PRUNING: uncomment to prune\n", + "# minCoreSizeForPrune=50,\n", + "# prune before simulation based on thermo\n", + "# toleranceThermoKeepSpeciesInEdge=0.5,\n", + "# prune rxns from edge that dont move into core\n", + "# minSpeciesExistIterationsForPrune=2,\n", + "# FILTERING: set so threshold is slightly larger than max rate constants\n", + "# filterReactions=True,\n", + "# filterThreshold=5e8, # default value\n", + ")\n", + "\n", + "options(\n", + " units='si',\n", + " saveRestartPeriod=None,\n", + " generateOutputHTML=True,\n", + " generatePlots=False,\n", + " saveEdgeSpecies=True,\n", + " saveSimulationProfiles=True,\n", + ")\n", + "\n", + "generatedSpeciesConstraints(\n", + " allowed=['input species','reaction libraries'],\n", + " maximumSurfaceSites=1,\n", + ")\n", + "\n", + "Using catalyst surface properties from metal 'Pt111'.\n", + "Using binding energies:\n", + "{'H': (-2.75368,'eV/molecule'), 'C': (-7.02516,'eV/molecule'), 'N': (-4.63225,'eV/molecule'), 'O': (-3.81153,'eV/molecule')}\n", + "Using surface site density: (2.483e-09,'mol/cm^2')\n", + "Coverage dependence is turned OFF\n", + "Warning: Initial gas mole fractions do not sum to one; renormalizing.\n", + "\n", + "\n", + "Surface reaction system 1\n", + "Gas phase mole fractions:\n", + " CH4 0.11867\n", + " O2 0.022822\n", + " Ar 0.85851\n", + "Total gas phase: 1 moles\n", + "Pressure: 1e+05 Pa\n", + "Temperature: 600.0 K\n", + "Reactor volume: 0.0499 m3\n", + "Surface/volume ratio: 1e+05 m2/m3\n", + "Surface site density: 2.48e-05 mol/m2\n", + "Surface sites in reactor: 0.124 moles\n", + "Initial surface coverages (and amounts):\n", + " X 1 = 0.12387 moles\n", + "Warning: Initial gas mole fractions do not sum to one; renormalizing.\n", + "\n", + "\n", + "Surface reaction system 2\n", + "Gas phase mole fractions:\n", + " CH4 0.20129\n", + " O2 0.1677\n", + " Ar 0.63101\n", + "Total gas phase: 1 moles\n", + "Pressure: 1e+05 Pa\n", + "Temperature: 2000.0 K\n", + "Reactor volume: 0.166 m3\n", + "Surface/volume ratio: 1e+05 m2/m3\n", + "Surface site density: 2.48e-05 mol/m2\n", + "Surface sites in reactor: 0.413 moles\n", + "Initial surface coverages (and amounts):\n", + " X 1 = 0.4129 moles\n", + "Warning: Generate Output HTML option was turned on. Note that this will slow down model generation.\n", + "Warning: Edge species saving was turned on. This will slow down model generation for large simulations.\n", + "\n", + "Loading transport library from PrimaryTransportLibrary.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from OneDMinN2.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from NOx2018.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from GRI-Mech.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from NIST_Fluorine.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport group database from /home/moon/rmg/RMG-database/input/transport/groups...\n", + "Loading kinetics library Surface/Methane/Deutschmann_Pt from /home/moon/rmg/RMG-database/input/kinetics/libraries/Surface/Methane/Deutschmann_Pt/reactions.py...\n", + "Loading kinetics library Surface/Methane/Vlachos_Pt111 from /home/moon/rmg/RMG-database/input/kinetics/libraries/Surface/Methane/Vlachos_Pt111/reactions.py...\n", + "Loading kinetics library BurkeH2O2inArHe from /home/moon/rmg/RMG-database/input/kinetics/libraries/BurkeH2O2inArHe/reactions.py...\n", + "Loading frequencies group database from /home/moon/rmg/RMG-database/input/statmech/groups...\n", + "Loading solvation thermodynamics group database from /home/moon/rmg/RMG-database/input/solvation/groups...\n", + "Loading thermodynamics library from covDepSurfaceThermoPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from surfaceThermoPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from primaryThermoLibrary.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from thermo_DFT_CCSDTF12_BAC.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from DFT_QCI_thermo.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics group database from /home/moon/rmg/RMG-database/input/thermo/groups...\n", + "Trimolecular reactions are turned on\n", + "Adding rate rules from training set in kinetics families...\n", + "Warning: Cphigh is above the theoretical CpInf value for ThermoData object\n", + "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), thermo_coverage_dependence={}, comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", + "The thermo for this species is probably wrong! Setting CpInf = Cphigh for Entropy calculationat T = 2000.0 K...\n", + "Warning: Cphigh is above the theoretical CpInf value for ThermoData object\n", + "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), thermo_coverage_dependence={}, comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", + "The thermo for this species is probably wrong! Setting CpInf = Cphigh for Entropy calculationat T = 1666.6666666666665 K...\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Filling in rate rules in kinetics families by averaging...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=9.78829e-18): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=8.30526e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.38146e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=8.43874e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=2.11249e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=5.2787e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=2.26538e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.9079e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=7.53573e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=7.43345e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.47964e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.34061e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=8.87325e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.20362e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=2.39961e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.56405e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=4.40143e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=4.71804e-22): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=4.97734e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=7.47076e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=6.10745e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.69387e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.30536e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.51603e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.18757e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/rmg/RMG-Py/rmgpy/thermo/thermoengine.py:100: LinAlgWarning: Ill-conditioned matrix (rcond=3.18757e-21): result may not be accurate.\n", + " thermo = wilhoit.to_nasa(Tmin=100.0, Tmax=5000.0, Tint=1000.0)\n", + "/home/moon/rmg/RMG-Py/rmgpy/thermo/thermoengine.py:100: LinAlgWarning: Ill-conditioned matrix (rcond=1.08953e-21): result may not be accurate.\n", + " thermo = wilhoit.to_nasa(Tmin=100.0, Tmax=5000.0, Tint=1000.0)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding reaction library Surface/Methane/Deutschmann_Pt to model edge...\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 18 new edge reactions\n", + " X(1) + X(1) + H2(6) <=> HX(21) + HX(21)\n", + " X(1) + X(1) + O2(3) <=> OX(25) + OX(25)\n", + " X(1) + X(1) + CH4(2) <=> HX(21) + CH3X(27)\n", + " X(1) + OX(25) + CH4(2) <=> OHX(31) + CH3X(27)\n", + " X(1) + OHX(31) + CH4(2) <=> H2OX(32) + CH3X(27)\n", + " X(1) + H2O(5) <=> H2OX(32)\n", + " X(1) + CO2(4) <=> CO2X(22)\n", + " X(1) + CO(7) <=> COX(23)\n", + " OX(25) + CX(29) <=> X(1) + COX(23)\n", + " OX(25) + COX(23) <=> X(1) + CO2X(22)\n", + " HX(21) + CO2X(22) <=> OHX(31) + COX(23)\n", + " HX(21) + CH2X(26) <=> X(1) + CH3X(27)\n", + " HX(21) + CHX(28) <=> X(1) + CH2X(26)\n", + " X(1) + CHX(28) <=> HX(21) + CX(29)\n", + " OX(25) + HX(21) <=> X(1) + OHX(31)\n", + " OX(25) + H2OX(32) <=> OHX(31) + OHX(31)\n", + " H2(6) + CX(29) <=> CH2X(26)\n", + " HX(21) + OHX(31) <=> X(1) + H2OX(32)\n", + "\n", + "After model enlargement:\n", + " The model core has 0 species and 0 reactions\n", + " The model edge has 34 species and 18 reactions\n", + "\n", + "Adding reaction library Surface/Methane/Vlachos_Pt111 to model edge...\n", + "This library reaction was not new: X(1) + X(1) + O2(3) <=> OX(25) + OX(25)\n", + "This library reaction was not new: X(1) + CO2(4) <=> CO2X(22)\n", + "This library reaction was not new: X(1) + CO2X(22) <=> OX(25) + COX(23)\n", + "This library reaction was not new: X(1) + X(1) + H2(6) <=> HX(21) + HX(21)\n", + "This library reaction was not new: X(1) + OHX(31) <=> OX(25) + HX(21)\n", + "This library reaction was not new: X(1) + H2OX(32) <=> HX(21) + OHX(31)\n", + "This library reaction was not new: OX(25) + H2OX(32) <=> OHX(31) + OHX(31)\n", + "This library reaction was not new: X(1) + H2O(5) <=> H2OX(32)\n", + "This library reaction was not new: HX(21) + CO2X(22) <=> OHX(31) + COX(23)\n", + "This library reaction was not new: X(1) + X(1) + CH4(2) <=> HX(21) + CH3X(27)\n", + "This library reaction was not new: X(1) + CH3X(27) <=> HX(21) + CH2X(26)\n", + "This library reaction was not new: X(1) + CH2X(26) <=> HX(21) + CHX(28)\n", + "This library reaction was not new: X(1) + CHX(28) <=> HX(21) + CX(29)\n", + "This library reaction was not new: X(1) + COX(23) <=> OX(25) + CX(29)\n", + "Warning: Species CO from reaction library Surface/Methane/Vlachos_Pt111 is globally forbidden. It will behave as an inert unless found in a seed mechanism or reaction library.\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 15 new edge species\n", + " O(34)\n", + " CO(35)\n", + " COOH(36)\n", + " COOH_X(37)\n", + " HCOO(38)\n", + " HCOO_XX(39)\n", + " C(40)\n", + " CH(41)\n", + " CH2(42)\n", + " CH3OH_X(43)\n", + " CH3O(44)\n", + " CH3O_X(45)\n", + " CH2O_X(46)\n", + " CH2OH(47)\n", + " CH2OH_X(48)\n", + "Added 0 new core reactions\n", + "Created 37 new edge reactions\n", + " X(1) + O(34) <=> OX(25)\n", + " X(1) + CO(35) <=> COX(23)\n", + " X(1) + OH(17) <=> OHX(31)\n", + " X(1) + H(12) <=> HX(21)\n", + " X(1) + COOH(36) <=> COOH_X(37)\n", + " X(1) + COOH_X(37) <=> OHX(31) + COX(23)\n", + " X(1) + COOH_X(37) <=> HX(21) + CO2X(22)\n", + " H2OX(32) + COX(23) <=> HX(21) + COOH_X(37)\n", + " OHX(31) + CO2X(22) <=> OX(25) + COOH_X(37)\n", + " H2OX(32) + CO2X(22) <=> OHX(31) + COOH_X(37)\n", + " X(1) + X(1) + HCOO(38) <=> HCOO_XX(39)\n", + " HX(21) + CO2X(22) <=> HCOO_XX(39)\n", + " X(1) + OHX(31) + CO2X(22) <=> OX(25) + HCOO_XX(39)\n", + " X(1) + C(40) <=> CX(29)\n", + " X(1) + CH(41) <=> CHX(28)\n", + " X(1) + CH2(42) <=> CH2X(26)\n", + " X(1) + CH3(10) <=> CH3X(27)\n", + " OX(25) + CH3X(27) <=> OHX(31) + CH2X(26)\n", + " OHX(31) + CHX(28) <=> OX(25) + CH2X(26)\n", + " OHX(31) + CX(29) <=> OX(25) + CHX(28)\n", + " H2OX(32) + CH2X(26) <=> OHX(31) + CH3X(27)\n", + " H2OX(32) + CHX(28) <=> OHX(31) + CH2X(26)\n", + " H2OX(32) + CX(29) <=> OHX(31) + CHX(28)\n", + " HX(21) + COX(23) <=> OX(25) + CHX(28)\n", + " HX(21) + COX(23) <=> OHX(31) + CX(29)\n", + " COX(23) + COX(23) <=> CX(29) + CO2X(22)\n", + " X(1) + CH3OH(14) <=> CH3OH_X(43)\n", + " X(1) + CH3O(44) <=> CH3O_X(45)\n", + " X(1) + CH2O(9) <=> CH2O_X(46)\n", + " X(1) + HCO(15) <=> CHOX(33)\n", + " X(1) + CH2OH(47) <=> CH2OH_X(48)\n", + " X(1) + CH3OH_X(43) <=> HX(21) + CH3O_X(45)\n", + " X(1) + CH3O_X(45) <=> HX(21) + CH2O_X(46)\n", + " X(1) + CH2O_X(46) <=> HX(21) + CHOX(33)\n", + " X(1) + CHOX(33) <=> HX(21) + COX(23)\n", + " X(1) + CH3OH_X(43) <=> HX(21) + CH2OH_X(48)\n", + " X(1) + CH2OH_X(48) <=> HX(21) + CH2O_X(46)\n", + "\n", + "After model enlargement:\n", + " The model core has 0 species and 0 reactions\n", + " The model edge has 49 species and 55 reactions\n", + "\n", + "Adding reaction library BurkeH2O2inArHe to model edge...\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 3 new edge species\n", + " He(49)\n", + " HO2(50)\n", + " H2O2(51)\n", + "Added 0 new core reactions\n", + "Created 30 new edge reactions\n", + " O2(3) + H(12) <=> O(34) + OH(17)\n", + " O(34) + H2(6) <=> H(12) + OH(17)\n", + " OH(17) + H2(6) <=> H(12) + H2O(5)\n", + " OH(17) + OH(17) <=> O(34) + H2O(5)\n", + " H2(6) <=> H(12) + H(12)\n", + " Ar + H2(6) <=> Ar + H(12) + H(12)\n", + " He(49) + H2(6) <=> He(49) + H(12) + H(12)\n", + " O(34) + O(34) <=> O2(3)\n", + " Ar + O(34) + O(34) <=> Ar + O2(3)\n", + " He(49) + O(34) + O(34) <=> He(49) + O2(3)\n", + " O(34) + H(12) <=> OH(17)\n", + " H2O(5) <=> H(12) + OH(17)\n", + " H2O(5) + H2O(5) <=> H(12) + OH(17) + H2O(5)\n", + " O2(3) + H(12) <=> HO2(50)\n", + " H(12) + HO2(50) <=> O2(3) + H2(6)\n", + " H(12) + HO2(50) <=> OH(17) + OH(17)\n", + " O(34) + HO2(50) <=> O2(3) + OH(17)\n", + " OH(17) + HO2(50) <=> O2(3) + H2O(5)\n", + " HO2(50) + HO2(50) <=> O2(3) + H2O2(51)\n", + " H2O2(51) <=> OH(17) + OH(17)\n", + " H(12) + H2O2(51) <=> OH(17) + H2O(5)\n", + " H(12) + H2O2(51) <=> HO2(50) + H2(6)\n", + " O(34) + H2O2(51) <=> OH(17) + HO2(50)\n", + " OH(17) + H2O2(51) <=> HO2(50) + H2O(5)\n", + " H(12) + HO2(50) <=> O(34) + H2O(5)\n", + " H(12) + HO2(50) <=> H2O2(51)\n", + " OH(17) + OH(17) <=> O2(3) + H2(6)\n", + " O(34) + H2O(5) <=> O2(3) + H2(6)\n", + " O(34) + H2O2(51) <=> O2(3) + H2O(5)\n", + " O(34) + OH(17) <=> HO2(50)\n", + "\n", + "After model enlargement:\n", + " The model core has 0 species and 0 reactions\n", + " The model edge has 52 species and 85 reactions\n", + "\n", + "Warning: Input species CH3CH is globally forbidden but will be explicitly allowed since input species are permitted by the user's species constraints\n", + "NOT generating reactions for unreactive species Ar\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " Ar\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 1 species and 0 reactions\n", + " The model edge has 51 species and 85 reactions\n", + "\n", + "\n", + "NOT generating reactions for unreactive species Ne\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " Ne\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 2 species and 0 reactions\n", + " The model edge has 51 species and 85 reactions\n", + "\n", + "\n", + "NOT generating reactions for unreactive species N2\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " N2\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 3 species and 0 reactions\n", + " The model edge has 51 species and 85 reactions\n", + "\n", + "\n", + "Adding species X(1) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " X(1)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 4 species and 0 reactions\n", + " The model edge has 50 species and 85 reactions\n", + "\n", + "\n", + "Adding species CH4(2) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH4(2)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 5 species and 0 reactions\n", + " The model edge has 49 species and 85 reactions\n", + "\n", + "\n", + "Adding species O2(3) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " O2(3)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 6 species and 0 reactions\n", + " The model edge has 48 species and 85 reactions\n", + "\n", + "\n", + "Adding species CO2(4) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CO2(4)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 7 species and 0 reactions\n", + " The model edge has 47 species and 85 reactions\n", + "\n", + "\n", + "Adding species H2O(5) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2O(5)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 8 species and 0 reactions\n", + " The model edge has 46 species and 85 reactions\n", + "\n", + "\n", + "Adding species H2(6) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2(6)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 9 species and 0 reactions\n", + " The model edge has 45 species and 85 reactions\n", + "\n", + "\n", + "Adding species CO(7) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CO(7)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 10 species and 0 reactions\n", + " The model edge has 44 species and 85 reactions\n", + "\n", + "\n", + "Adding species C2H6(8) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C2H6(8)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 11 species and 0 reactions\n", + " The model edge has 43 species and 85 reactions\n", + "\n", + "\n", + "Adding species CH2O(9) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH2O(9)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 12 species and 0 reactions\n", + " The model edge has 42 species and 85 reactions\n", + "\n", + "\n", + "Adding species CH3(10) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3(10)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 13 species and 0 reactions\n", + " The model edge has 41 species and 85 reactions\n", + "\n", + "\n", + "Adding species C3H8(11) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C3H8(11)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 14 species and 0 reactions\n", + " The model edge has 40 species and 85 reactions\n", + "\n", + "\n", + "Adding species H(12) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H(12)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " H2(6) <=> H(12) + H(12)\n", + " Ar + H2(6) <=> Ar + H(12) + H(12)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 15 species and 2 reactions\n", + " The model edge has 39 species and 83 reactions\n", + "\n", + "\n", + "Adding species C2H5(13) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C2H5(13)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 16 species and 2 reactions\n", + " The model edge has 38 species and 83 reactions\n", + "\n", + "\n", + "Adding species CH3OH(14) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3OH(14)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 17 species and 2 reactions\n", + " The model edge has 37 species and 83 reactions\n", + "\n", + "\n", + "Adding species HCO(15) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " HCO(15)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 18 species and 2 reactions\n", + " The model edge has 36 species and 83 reactions\n", + "\n", + "\n", + "Adding species CH3CHO(16) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3CHO(16)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 19 species and 2 reactions\n", + " The model edge has 35 species and 83 reactions\n", + "\n", + "\n", + "Adding species OH(17) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " OH(17)\n", + "Created 0 new edge species\n", + "Moved 4 reactions from edge to core\n", + " OH(17) + H2(6) <=> H(12) + H2O(5)\n", + " H2O(5) <=> H(12) + OH(17)\n", + " H2O(5) + H2O(5) <=> H(12) + OH(17) + H2O(5)\n", + " OH(17) + OH(17) <=> O2(3) + H2(6)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 20 species and 6 reactions\n", + " The model edge has 34 species and 79 reactions\n", + "\n", + "\n", + "Adding species C2H4(18) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C2H4(18)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 21 species and 6 reactions\n", + " The model edge has 33 species and 79 reactions\n", + "\n", + "\n", + "Adding species CH3CH(19) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3CH(19)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 22 species and 6 reactions\n", + " The model edge has 32 species and 79 reactions\n", + "\n", + "\n", + "Adding species CH3OO(20) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3OO(20)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 23 species and 6 reactions\n", + " The model edge has 31 species and 79 reactions\n", + "\n", + "\n", + "Adding species HX(21) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " HX(21)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " X(1) + X(1) + H2(6) <=> HX(21) + HX(21)\n", + " X(1) + H(12) <=> HX(21)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 24 species and 8 reactions\n", + " The model edge has 30 species and 77 reactions\n", + "\n", + "\n", + "Adding species CO2X(22) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CO2X(22)\n", + "Created 0 new edge species\n", + "Moved 1 reactions from edge to core\n", + " X(1) + CO2(4) <=> CO2X(22)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 25 species and 9 reactions\n", + " The model edge has 29 species and 76 reactions\n", + "\n", + "\n", + "Adding species COX(23) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " COX(23)\n", + "Created 0 new edge species\n", + "Moved 1 reactions from edge to core\n", + " X(1) + CO(7) <=> COX(23)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 26 species and 10 reactions\n", + " The model edge has 28 species and 75 reactions\n", + "\n", + "\n", + "Adding species CH4X(24) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH4X(24)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 27 species and 10 reactions\n", + " The model edge has 27 species and 75 reactions\n", + "\n", + "\n", + "Adding species OX(25) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " OX(25)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " X(1) + X(1) + O2(3) <=> OX(25) + OX(25)\n", + " OX(25) + COX(23) <=> X(1) + CO2X(22)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 28 species and 12 reactions\n", + " The model edge has 26 species and 73 reactions\n", + "\n", + "\n", + "Adding species CH2X(26) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH2X(26)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 29 species and 12 reactions\n", + " The model edge has 25 species and 73 reactions\n", + "\n", + "\n", + "Adding species CH3X(27) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3X(27)\n", + "Created 0 new edge species\n", + "Moved 3 reactions from edge to core\n", + " X(1) + X(1) + CH4(2) <=> HX(21) + CH3X(27)\n", + " HX(21) + CH2X(26) <=> X(1) + CH3X(27)\n", + " X(1) + CH3(10) <=> CH3X(27)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 30 species and 15 reactions\n", + " The model edge has 24 species and 70 reactions\n", + "\n", + "\n", + "Adding species CHX(28) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CHX(28)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " HX(21) + CHX(28) <=> X(1) + CH2X(26)\n", + " HX(21) + COX(23) <=> OX(25) + CHX(28)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 31 species and 17 reactions\n", + " The model edge has 23 species and 68 reactions\n", + "\n", + "\n", + "Adding species CX(29) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CX(29)\n", + "Created 0 new edge species\n", + "Moved 4 reactions from edge to core\n", + " OX(25) + CX(29) <=> X(1) + COX(23)\n", + " X(1) + CHX(28) <=> HX(21) + CX(29)\n", + " H2(6) + CX(29) <=> CH2X(26)\n", + " COX(23) + COX(23) <=> CX(29) + CO2X(22)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 32 species and 21 reactions\n", + " The model edge has 22 species and 64 reactions\n", + "\n", + "\n", + "Adding species H2X(30) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2X(30)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 33 species and 21 reactions\n", + " The model edge has 21 species and 64 reactions\n", + "\n", + "\n", + "Adding species OHX(31) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " OHX(31)\n", + "Created 0 new edge species\n", + "Moved 8 reactions from edge to core\n", + " X(1) + OX(25) + CH4(2) <=> OHX(31) + CH3X(27)\n", + " HX(21) + CO2X(22) <=> OHX(31) + COX(23)\n", + " OX(25) + HX(21) <=> X(1) + OHX(31)\n", + " X(1) + OH(17) <=> OHX(31)\n", + " OX(25) + CH3X(27) <=> OHX(31) + CH2X(26)\n", + " OHX(31) + CHX(28) <=> OX(25) + CH2X(26)\n", + " OHX(31) + CX(29) <=> OX(25) + CHX(28)\n", + " HX(21) + COX(23) <=> OHX(31) + CX(29)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 34 species and 29 reactions\n", + " The model edge has 20 species and 56 reactions\n", + "\n", + "\n", + "Adding species H2OX(32) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2OX(32)\n", + "Created 0 new edge species\n", + "Moved 7 reactions from edge to core\n", + " X(1) + OHX(31) + CH4(2) <=> H2OX(32) + CH3X(27)\n", + " X(1) + H2O(5) <=> H2OX(32)\n", + " OX(25) + H2OX(32) <=> OHX(31) + OHX(31)\n", + " HX(21) + OHX(31) <=> X(1) + H2OX(32)\n", + " H2OX(32) + CH2X(26) <=> OHX(31) + CH3X(27)\n", + " H2OX(32) + CHX(28) <=> OHX(31) + CH2X(26)\n", + " H2OX(32) + CX(29) <=> OHX(31) + CHX(28)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 35 species and 36 reactions\n", + " The model edge has 19 species and 49 reactions\n", + "\n", + "\n", + "Adding species CHOX(33) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CHOX(33)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " X(1) + HCO(15) <=> CHOX(33)\n", + " X(1) + CHOX(33) <=> HX(21) + COX(23)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 36 species and 38 reactions\n", + " The model edge has 18 species and 47 reactions\n", + "\n", + "\n", + "Initialization complete. Starting model generation.\n", + "\n", + "Generating initial reactions...\n", + "For reaction generation 1 process is used.\n", + "For reaction O2(3) + CH4(2) <=> HO2(50) + CH3(10), Ea raised from 216.4 to 231.5 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CO2(4) <=> [O]OO[C]=O(82), Ea raised from 413.6 to 418.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CO2(4) <=> [O]OC([O])=O(83), Ea raised from 298.1 to 300.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H6(8) <=> HO2(50) + C2H5(13), Ea raised from 207.3 to 215.7 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH2O(9) <=> HO2(50) + HCO(15), Ea raised from 158.7 to 162.5 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH2O(9) <=> [CH2]OO[O](84), Ea raised from 358.5 to 363.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH2O(9) <=> [O]CO[O](85), Ea raised from 172.4 to 176.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C3H8(11) <=> HO2(50) + C[CH]C(53), Ea raised from 201.7 to 205.6 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3OH(14) <=> HO2(50) + CH2OH(47), Ea raised from 178.9 to 195.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3CHO(16) <=> HO2(50) + C[C]=O(59), Ea raised from 157.1 to 167.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3CHO(16) <=> C[CH]OO[O](88), Ea raised from 373.4 to 376.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3CHO(16) <=> CC([O])O[O](89), Ea raised from 176.0 to 178.9 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + OH(17) <=> O=O(90) + OH(17), Ea raised from 94.3 to 94.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H4(18) <=> HO2(50) + [CH]=C(60), Ea raised from 251.1 to 256.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H4(18) <=> [CH2]CO[O](91), Ea raised from 132.2 to 156.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3OO(20) <=> COOO[O](93), Ea raised from 77.5 to 83.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CO2(4) <=> O=C1OOC1=O(97), Ea raised from 333.9 to 341.4 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH2O(9) <=> O=C1COO1(106), Ea raised from 264.6 to 271.9 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3(10) <=> CC([O])=O(108), Ea raised from 58.1 to 65.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction H(12) + CO2(4) <=> HCOO(38), Ea raised from 47.9 to 52.0 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + HCO(15) <=> [O]C(=O)C=O(122), Ea raised from 146.3 to 150.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3CHO(16) <=> CC1OOC1=O(129), Ea raised from 271.9 to 279.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3CH(19) <=> C[CH]O[C]=O(141), Ea raised from 41.0 to 43.4 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3OO(20) <=> COOO[C]=O(145), Ea raised from 251.4 to 255.6 kJ/mol to match endothermicity of reaction.\n", + "For reaction CH2O(9) + CH2O(9) <=> C1COO1(169), Ea raised from 222.9 to 224.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction CH2O(9) + CH3CHO(16) <=> CC1COO1(179), Ea raised from 243.0 to 245.6 kJ/mol to match endothermicity of reaction.\n", + "For reaction CH2O(9) + CH3OO(20) <=> [CH2]OOOC(188), Ea raised from 196.3 to 200.6 kJ/mol to match endothermicity of reaction.\n", + "For reaction COO(81) + [CH2]CC(54) <=> CH3OO(20) + C3H8(11) Ea raised from -7.3 to -7.3 kJ/mol.\n", + "For reaction CH3CHO(16) + CH3CHO(16) <=> CC1OOC1C(208), Ea raised from 263.7 to 267.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction CH3OO(20) + CH3CHO(16) <=> C[CH]OOOC(218), Ea raised from 211.1 to 214.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction HX(21) + CO2X(22) <=> X(1) + O=C[O][Pt](227), Ea raised from 83.9 to 85.5 kJ/mol to match endothermicity of reaction.\n", + "For reaction OX(25) + H2OX(32) <=> HX(21) + O[O][Pt](246), Ea raised from 247.3 to 247.4 kJ/mol to match endothermicity of reaction.\n", + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 201 new edge species\n", + " [CH2](52)\n", + " C[CH]C(53)\n", + " [CH2]CC(54)\n", + " [CH2][CH2](55)\n", + " [CH2]C[O](56)\n", + " C=CO(57)\n", + " [CH2]C=O(58)\n", + " C[C]=O(59)\n", + " [CH]=C(60)\n", + " [CH]C(61)\n", + " [CH][CH2](62)\n", + " [CH2]O[O](63)\n", + " C[CH2][Pt](64)\n", + " CC.[Pt](65)\n", + " C[CH](C)[Pt](66)\n", + " CC[CH2][Pt](67)\n", + " CCC.[Pt](68)\n", + " O=C[CH2][Pt](69)\n", + " C[C](=O)[Pt](70)\n", + " C[CH]=[Pt](71)\n", + " CC=O.[Pt](72)\n", + " C=[CH][Pt](73)\n", + " C=C.[Pt](74)\n", + " CO[O][Pt](75)\n", + " COC=O(76)\n", + " CC(=O)O(77)\n", + " [CH]O(78)\n", + " C[CH]O(79)\n", + " CC[O](80)\n", + " COO(81)\n", + " [O]OO[C]=O(82)\n", + " [O]OC([O])=O(83)\n", + " [CH2]OO[O](84)\n", + " [O]CO[O](85)\n", + " CCO[O](86)\n", + " [O]OC=O(87)\n", + " C[CH]OO[O](88)\n", + " CC([O])O[O](89)\n", + " O=O(90)\n", + " [CH2]CO[O](91)\n", + " C[CH]O[O](92)\n", + " COOO[O](93)\n", + " [O]C(=O)C([O])=O(94)\n", + " [O]C(=O)O[C]=O(95)\n", + " O=[C]OO[C]=O(96)\n", + " O=C1OOC1=O(97)\n", + " O=CO(98)\n", + " COC(C)=O(99)\n", + " CCOC=O(100)\n", + " CCC(=O)O(101)\n", + " [O]CC([O])=O(102)\n", + " [CH2]OC([O])=O(103)\n", + " [O]CO[C]=O(104)\n", + " [CH2]OO[C]=O(105)\n", + " O=C1COO1(106)\n", + " CO[C]=O(107)\n", + " CC([O])=O(108)\n", + " CCOC(C)=O(109)\n", + " CC(C)OC=O(110)\n", + " CCC(=O)OC(111)\n", + " CCCOC=O(112)\n", + " CC(C)C(=O)O(113)\n", + " CCCC(=O)O(114)\n", + " [CH2]COC=O(115)\n", + " [CH2]CC(=O)O(116)\n", + " CCO[C]=O(117)\n", + " CCC([O])=O(118)\n", + " O=COCO(119)\n", + " O=C(O)CO(120)\n", + " O=[C]OC=O(121)\n", + " [O]C(=O)C=O(122)\n", + " O=CCOC=O(123)\n", + " O=CCC(=O)O(124)\n", + " CC([O])C([O])=O(125)\n", + " C[CH]OC([O])=O(126)\n", + " CC([O])O[C]=O(127)\n", + " C[CH]OO[C]=O(128)\n", + " CC1OOC1=O(129)\n", + " C=COOC=O(130)\n", + " C=COC(=O)O(131)\n", + " [O]C(=O)O(132)\n", + " C=COC=O(133)\n", + " C=CC(=O)O(134)\n", + " [CH2]CC([O])=O(135)\n", + " [CH2]CO[C]=O(136)\n", + " O=C1CCO1(137)\n", + " [CH]COC=O(138)\n", + " [CH]CC(=O)O(139)\n", + " [C]C(140)\n", + " C[CH]O[C]=O(141)\n", + " CC=C([O])[O](142)\n", + " [O]OCOC=O(143)\n", + " [O]OCC(=O)O(144)\n", + " COOO[C]=O(145)\n", + " COOC([O])=O(146)\n", + " CCO(147)\n", + " CCC=O(148)\n", + " CC(C)=O(149)\n", + " O=CC=O(150)\n", + " CC(C)C=O(151)\n", + " CCCC=O(152)\n", + " CCC(C)=O(153)\n", + " [CH2]CC=O(154)\n", + " CC[C]=O(155)\n", + " O=CCO(156)\n", + " O=[C]C=O(157)\n", + " O=CCC=O(158)\n", + " CC(=O)C=O(159)\n", + " [CH-]=[O+]OC=C(160)\n", + " C=C[O+]=[C-]O(161)\n", + " C=CC=O(162)\n", + " [CH]CC=O(163)\n", + " [O]OCC=O(164)\n", + " COO[C]=O(165)\n", + " [O]CC[O](166)\n", + " [CH2]OC[O](167)\n", + " [CH2]OO[CH2](168)\n", + " C1COO1(169)\n", + " [CH2]OC(170)\n", + " [CH2]OCC(171)\n", + " CCC[O](172)\n", + " [CH2]OC=O(173)\n", + " [O]CC=O(174)\n", + " CC([O])C[O](175)\n", + " C[CH]OC[O](176)\n", + " [CH2]OC(C)[O](177)\n", + " [CH2]OO[CH]C(178)\n", + " CC1COO1(179)\n", + " C=COOC(180)\n", + " C=COCO(181)\n", + " [O]CO(182)\n", + " [CH2]CC[O](183)\n", + " [CH2]CO[CH2](184)\n", + " C1COC1(185)\n", + " [CH2]O[CH]C(186)\n", + " C[CH]C[O](187)\n", + " [CH2]OOOC(188)\n", + " COOC[O](189)\n", + " C[CH]OC(190)\n", + " CC(C)[O](191)\n", + " COOC(192)\n", + " CCCC(193)\n", + " C[CH]OCC(194)\n", + " CCC(C)[O](195)\n", + " [CH2]CCC(196)\n", + " C[CH]CC(197)\n", + " CCOOC(198)\n", + " CCOC(199)\n", + " CCCO(200)\n", + " C[CH]OC=O(201)\n", + " CC([O])C=O(202)\n", + " C[CH]C=O(203)\n", + " COOC=O(204)\n", + " CC([O])C(C)[O](205)\n", + " C[CH]OC(C)[O](206)\n", + " C[CH]OO[CH]C(207)\n", + " CC1OOC1C(208)\n", + " C=COOCC(209)\n", + " C=COC(C)O(210)\n", + " CC([O])O(211)\n", + " [CH2]CC(C)[O](212)\n", + " [CH2]CO[CH]C(213)\n", + " CC1CCO1(214)\n", + " C=COCC(215)\n", + " C[CH]O[CH]C(216)\n", + " C[CH]C(C)[O](217)\n", + " C[CH]OOOC(218)\n", + " COOC(C)[O](219)\n", + " [CH2]CO(220)\n", + " COOO(221)\n", + " [CH2]CC[CH2](222)\n", + " [CH2]C[CH]C(223)\n", + " [CH2]COOC(224)\n", + " C[CH]OOC(225)\n", + " COOOOC(226)\n", + " O=C[O][Pt](227)\n", + " O[C]#[Pt](228)\n", + " O[CH]=[Pt](229)\n", + " CC(=O)[O][Pt](230)\n", + " CO[C](=O)[Pt](231)\n", + " O=C(O)[O][Pt](232)\n", + " O=[C]([Pt])OO(233)\n", + " O=CC(=O)[O][Pt](234)\n", + " O=CO[C](=O)[Pt](235)\n", + " O=C=C=O(236)\n", + " O=C=C=O.[Pt](237)\n", + " C=C=O(238)\n", + " C=C=O.[Pt](239)\n", + " O=C=[CH][Pt](240)\n", + " O=C=[C]=[Pt](241)\n", + " OO[C]#[Pt](242)\n", + " O=C[C](=O)[Pt](243)\n", + " C[C]#[Pt](244)\n", + " O=O.[Pt](245)\n", + " O[O][Pt](246)\n", + " C=[C]=[Pt](247)\n", + " O=C[CH]=[Pt](248)\n", + " O=C[C]#[Pt](249)\n", + " OO[CH]=[Pt](250)\n", + " O=CO.[Pt](251)\n", + " O=CC=O.[Pt](252)\n", + "Added 71 new core reactions\n", + " H(12) + CH3(10) <=> CH4(2)\n", + " CH3(10) + CH3(10) <=> C2H6(8)\n", + " H(12) + C2H5(13) <=> C2H6(8)\n", + " H2(6) + CO(7) <=> CH2O(9)\n", + " H(12) + HCO(15) <=> CH2O(9)\n", + " CH3(10) + C2H5(13) <=> C3H8(11)\n", + " H(12) + CH3CH(19) <=> C2H5(13)\n", + " H(12) + C2H4(18) <=> C2H5(13)\n", + " OH(17) + CH3(10) <=> CH3OH(14)\n", + " H(12) + CO(7) <=> HCO(15)\n", + " CO(7) + CH4(2) <=> CH3CHO(16)\n", + " HCO(15) + CH3(10) <=> CH3CHO(16)\n", + " CH3OO(20) <=> O2(3) + CH3(10)\n", + " CH3OO(20) <=> OH(17) + CH2O(9)\n", + " X(1) + CH4(2) <=> CH4X(24)\n", + " X(1) + H2(6) <=> H2X(30)\n", + " X(1) + X(1) + CO2(4) <=> OX(25) + COX(23)\n", + " X(1) + X(1) + H2O(5) <=> HX(21) + OHX(31)\n", + " X(1) + X(1) + C2H6(8) <=> CH3X(27) + CH3X(27)\n", + " X(1) + X(1) + CH2O(9) <=> HX(21) + CHOX(33)\n", + " X(1) + X(1) + CH2O(9) <=> OX(25) + CH2X(26)\n", + " X(1) + X(1) + CH3OH(14) <=> OHX(31) + CH3X(27)\n", + " X(1) + X(1) + CH3CHO(16) <=> CHOX(33) + CH3X(27)\n", + " X(1) + X(1) + C2H4(18) <=> CH2X(26) + CH2X(26)\n", + " X(1) + CH4X(24) <=> HX(21) + CH3X(27)\n", + " X(1) + CHOX(33) <=> OX(25) + CHX(28)\n", + " HCO(15) + CH3(10) <=> CO(7) + CH4(2)\n", + " H(12) + CH4(2) <=> H2(6) + CH3(10)\n", + " CH3(10) + C2H6(8) <=> CH4(2) + C2H5(13)\n", + " HCO(15) + CH4(2) <=> CH2O(9) + CH3(10)\n", + " OH(17) + CH4(2) <=> H2O(5) + CH3(10)\n", + " CH3(10) + C2H5(13) <=> CH4(2) + C2H4(18)\n", + " CH4(2) + CH3CH(19) <=> CH3(10) + C2H5(13)\n", + " OH(17) + HCO(15) <=> H2O(5) + CO(7)\n", + " OH(17) + C2H6(8) <=> H2O(5) + C2H5(13)\n", + " OH(17) + CH2O(9) <=> H2O(5) + HCO(15)\n", + " OH(17) + C2H5(13) <=> H2O(5) + C2H4(18)\n", + " OH(17) + C2H5(13) <=> H2O(5) + CH3CH(19)\n", + " H(12) + HCO(15) <=> H2(6) + CO(7)\n", + " H(12) + C2H6(8) <=> H2(6) + C2H5(13)\n", + " H2(6) + HCO(15) <=> H(12) + CH2O(9)\n", + " H(12) + C2H5(13) <=> H2(6) + C2H4(18)\n", + " H2(6) + CH3CH(19) <=> H(12) + C2H5(13)\n", + " HCO(15) + C2H5(13) <=> CO(7) + C2H6(8)\n", + " HCO(15) + HCO(15) <=> CO(7) + CH2O(9)\n", + " HCO(15) + CH3CH(19) <=> CO(7) + C2H5(13)\n", + " HCO(15) + C2H6(8) <=> CH2O(9) + C2H5(13)\n", + " C2H5(13) + C2H5(13) <=> C2H4(18) + C2H6(8)\n", + " CH3CH(19) + C2H6(8) <=> C2H5(13) + C2H5(13)\n", + " HCO(15) + C2H5(13) <=> CH2O(9) + C2H4(18)\n", + " CH2O(9) + CH3CH(19) <=> HCO(15) + C2H5(13)\n", + " CH3CH(19) + C2H5(13) <=> C2H4(18) + C2H5(13)\n", + " H2X(30) + CHX(28) <=> HX(21) + CH2X(26)\n", + " H2X(30) + CH2X(26) <=> HX(21) + CH3X(27)\n", + " H2X(30) + CX(29) <=> HX(21) + CHX(28)\n", + " OX(25) + H2X(30) <=> HX(21) + OHX(31)\n", + " H2X(30) + COX(23) <=> HX(21) + CHOX(33)\n", + " COX(23) + CH4X(24) <=> CHOX(33) + CH3X(27)\n", + " CHX(28) + CHOX(33) <=> COX(23) + CH2X(26)\n", + " CHOX(33) + CH2X(26) <=> COX(23) + CH3X(27)\n", + " CX(29) + CHOX(33) <=> COX(23) + CHX(28)\n", + " OX(25) + CHOX(33) <=> OHX(31) + COX(23)\n", + " OHX(31) + CHOX(33) <=> H2OX(32) + COX(23)\n", + " OX(25) + CH4X(24) <=> OHX(31) + CH3X(27)\n", + " CH2X(26) + CH4X(24) <=> CH3X(27) + CH3X(27)\n", + " CHX(28) + CH4X(24) <=> CH2X(26) + CH3X(27)\n", + " CX(29) + CH4X(24) <=> CHX(28) + CH3X(27)\n", + " OHX(31) + CH4X(24) <=> H2OX(32) + CH3X(27)\n", + " CH2X(26) + CH2X(26) <=> CHX(28) + CH3X(27)\n", + " CX(29) + CH3X(27) <=> CHX(28) + CH2X(26)\n", + " CX(29) + CH2X(26) <=> CHX(28) + CHX(28)\n", + "Created 566 new edge reactions\n", + " H2(6) + [CH2](52) <=> CH4(2)\n", + " [CH2](52) + CH4(2) <=> C2H6(8)\n", + " H(12) + CH2(42) <=> CH3(10)\n", + " [CH2](52) + C2H6(8) <=> C3H8(11)\n", + " [CH2](52) + C2H6(8) <=> C3H8(11)\n", + " H(12) + C[CH]C(53) <=> C3H8(11)\n", + " H(12) + [CH2]CC(54) <=> C3H8(11)\n", + " CH2(42) + CH3(10) <=> C2H5(13)\n", + " H(12) + [CH2][CH2](55) <=> C2H5(13)\n", + " H2O(5) + [CH2](52) <=> CH3OH(14)\n", + " H(12) + CH3O(44) <=> CH3OH(14)\n", + " H(12) + CH2OH(47) <=> CH3OH(14)\n", + " H(12) + CO(35) <=> HCO(15)\n", + " [CH2]C[O](56) <=> CH3CHO(16)\n", + " C=CO(57) <=> CH3CHO(16)\n", + " H(12) + [CH2]C=O(58) <=> CH3CHO(16)\n", + " H(12) + C[C]=O(59) <=> CH3CHO(16)\n", + " H(12) + [CH]=C(60) <=> C2H4(18)\n", + " [CH]C(61) <=> C2H4(18)\n", + " H(12) + [CH][CH2](62) <=> CH3CH(19)\n", + " HO2(50) + [CH2](52) <=> CH3OO(20)\n", + " O(34) + CH3O(44) <=> CH3OO(20)\n", + " H(12) + [CH2]O[O](63) <=> CH3OO(20)\n", + " X(1) + X(1) + C2H6(8) <=> HX(21) + C[CH2][Pt](64)\n", + " X(1) + C2H6(8) <=> CC.[Pt](65)\n", + " X(1) + X(1) + C3H8(11) <=> CH3X(27) + C[CH2][Pt](64)\n", + " X(1) + X(1) + C3H8(11) <=> HX(21) + C[CH](C)[Pt](66)\n", + " X(1) + X(1) + C3H8(11) <=> HX(21) + CC[CH2][Pt](67)\n", + " X(1) + C3H8(11) <=> CCC.[Pt](68)\n", + " X(1) + C2H5(13) <=> C[CH2][Pt](64)\n", + " X(1) + X(1) + CH3OH(14) <=> HX(21) + CH3O_X(45)\n", + " X(1) + X(1) + CH3OH(14) <=> HX(21) + CH2OH_X(48)\n", + " X(1) + X(1) + CH3CHO(16) <=> HX(21) + O=C[CH2][Pt](69)\n", + " X(1) + X(1) + CH3CHO(16) <=> HX(21) + C[C](=O)[Pt](70)\n", + " X(1) + X(1) + CH3CHO(16) <=> OX(25) + C[CH]=[Pt](71)\n", + " X(1) + CH3CHO(16) <=> CC=O.[Pt](72)\n", + " X(1) + X(1) + C2H4(18) <=> HX(21) + C=[CH][Pt](73)\n", + " X(1) + C2H4(18) <=> C=C.[Pt](74)\n", + " X(1) + CH3OO(20) <=> CO[O][Pt](75)\n", + " O2(3) + CH4(2) <=> HO2(50) + CH3(10)\n", + " CO2(4) + CH4(2) <=> COC=O(76)\n", + " CO2(4) + CH4(2) <=> CC(=O)O(77)\n", + " COOH(36) + CH3(10) <=> CO2(4) + CH4(2)\n", + " HCOO(38) + CH3(10) <=> CO2(4) + CH4(2)\n", + " CH3(10) + CH2OH(47) <=> CH2O(9) + CH4(2)\n", + " CH3(10) + CH3O(44) <=> CH2O(9) + CH4(2)\n", + " [CH]O(78) + CH3(10) <=> HCO(15) + CH4(2)\n", + " CH3(10) + C[CH]O(79) <=> CH4(2) + CH3CHO(16)\n", + " CH3(10) + CC[O](80) <=> CH4(2) + CH3CHO(16)\n", + " CH3OO(20) + CH4(2) <=> CH3(10) + COO(81)\n", + " O2(3) + CO2(4) <=> [O]OO[C]=O(82)\n", + " O2(3) + CO2(4) <=> [O]OC([O])=O(83)\n", + " O2(3) + C2H6(8) <=> HO2(50) + C2H5(13)\n", + " O2(3) + CH2O(9) <=> HO2(50) + HCO(15)\n", + " O2(3) + CH2O(9) <=> [CH2]OO[O](84)\n", + " O2(3) + CH2O(9) <=> [O]CO[O](85)\n", + " HO2(50) + CH2(42) <=> O2(3) + CH3(10)\n", + " O2(3) + C3H8(11) <=> HO2(50) + C[CH]C(53)\n", + " HO2(50) + [CH2]CC(54) <=> O2(3) + C3H8(11)\n", + " O2(3) + C2H5(13) <=> HO2(50) + C2H4(18)\n", + " HO2(50) + CH3CH(19) <=> O2(3) + C2H5(13)\n", + " CCO[O](86) <=> O2(3) + C2H5(13)\n", + " HO2(50) + CH3O(44) <=> O2(3) + CH3OH(14)\n", + " O2(3) + CH3OH(14) <=> HO2(50) + CH2OH(47)\n", + " O2(3) + HCO(15) <=> HO2(50) + CO(7)\n", + " HO2(50) + CO(35) <=> O2(3) + HCO(15)\n", + " O2(3) + HCO(15) <=> [O]OC=O(87)\n", + " HO2(50) + [CH2]C=O(58) <=> O2(3) + CH3CHO(16)\n", + " O2(3) + CH3CHO(16) <=> HO2(50) + C[C]=O(59)\n", + " O2(3) + CH3CHO(16) <=> C[CH]OO[O](88)\n", + " O2(3) + CH3CHO(16) <=> CC([O])O[O](89)\n", + " O2(3) + OH(17) <=> O=O(90) + OH(17)\n", + " O2(3) + C2H4(18) <=> HO2(50) + [CH]=C(60)\n", + " O2(3) + C2H4(18) <=> [CH2]CO[O](91)\n", + " O2(3) + CH3CH(19) <=> C[CH]O[O](92)\n", + " O2(3) + CH3CH(19) <=> HO2(50) + [CH]=C(60)\n", + " HO2(50) + [CH][CH2](62) <=> O2(3) + CH3CH(19)\n", + " O2(3) + CH3OO(20) <=> HO2(50) + [CH2]O[O](63)\n", + " O2(3) + CH3OO(20) <=> COOO[O](93)\n", + " [O]C(=O)C([O])=O(94) <=> CO2(4) + CO2(4)\n", + " [O]C(=O)O[C]=O(95) <=> CO2(4) + CO2(4)\n", + " O=[C]OO[C]=O(96) <=> CO2(4) + CO2(4)\n", + " CO2(4) + CO2(4) <=> O=C1OOC1=O(97)\n", + " OH(17) + COOH(36) <=> H2O(5) + CO2(4)\n", + " OH(17) + HCOO(38) <=> H2O(5) + CO2(4)\n", + " H2(6) + CO2(4) <=> O=CO(98)\n", + " H(12) + COOH(36) <=> H2(6) + CO2(4)\n", + " H(12) + HCOO(38) <=> H2(6) + CO2(4)\n", + " CO2(4) + C2H6(8) <=> COC(C)=O(99)\n", + " CO2(4) + C2H6(8) <=> CCOC=O(100)\n", + " CO2(4) + C2H6(8) <=> CCC(=O)O(101)\n", + " COOH(36) + C2H5(13) <=> CO2(4) + C2H6(8)\n", + " HCOO(38) + C2H5(13) <=> CO2(4) + C2H6(8)\n", + " [O]CC([O])=O(102) <=> CO2(4) + CH2O(9)\n", + " [CH2]OC([O])=O(103) <=> CO2(4) + CH2O(9)\n", + " [O]CO[C]=O(104) <=> CO2(4) + CH2O(9)\n", + " [CH2]OO[C]=O(105) <=> CO2(4) + CH2O(9)\n", + " CO2(4) + CH2O(9) <=> O=C1COO1(106)\n", + " HCO(15) + COOH(36) <=> CO2(4) + CH2O(9)\n", + " HCO(15) + HCOO(38) <=> CO2(4) + CH2O(9)\n", + " COOH(36) + CH2(42) <=> CO2(4) + CH3(10)\n", + " HCOO(38) + CH2(42) <=> CO2(4) + CH3(10)\n", + " CO2(4) + CH3(10) <=> CO[C]=O(107)\n", + " CO2(4) + CH3(10) <=> CC([O])=O(108)\n", + " CO2(4) + C3H8(11) <=> CCOC(C)=O(109)\n", + " CO2(4) + C3H8(11) <=> CC(C)OC=O(110)\n", + " CO2(4) + C3H8(11) <=> CCC(=O)OC(111)\n", + " CO2(4) + C3H8(11) <=> CCCOC=O(112)\n", + " CO2(4) + C3H8(11) <=> CC(C)C(=O)O(113)\n", + " CO2(4) + C3H8(11) <=> CCCC(=O)O(114)\n", + " COOH(36) + C[CH]C(53) <=> CO2(4) + C3H8(11)\n", + " COOH(36) + [CH2]CC(54) <=> CO2(4) + C3H8(11)\n", + " HCOO(38) + C[CH]C(53) <=> CO2(4) + C3H8(11)\n", + " HCOO(38) + [CH2]CC(54) <=> CO2(4) + C3H8(11)\n", + " H(12) + CO2(4) <=> COOH(36)\n", + " H(12) + CO2(4) <=> HCOO(38)\n", + " CO2(4) + C2H5(13) <=> [CH2]COC=O(115)\n", + " CO2(4) + C2H5(13) <=> [CH2]CC(=O)O(116)\n", + " COOH(36) + [CH2][CH2](55) <=> CO2(4) + C2H5(13)\n", + " COOH(36) + CH3CH(19) <=> CO2(4) + C2H5(13)\n", + " HCOO(38) + [CH2][CH2](55) <=> CO2(4) + C2H5(13)\n", + " HCOO(38) + CH3CH(19) <=> CO2(4) + C2H5(13)\n", + " CO2(4) + C2H5(13) <=> CCO[C]=O(117)\n", + " CO2(4) + C2H5(13) <=> CCC([O])=O(118)\n", + " CO2(4) + CH3OH(14) <=> O=COCO(119)\n", + " CO2(4) + CH3OH(14) <=> O=C(O)CO(120)\n", + " COOH(36) + CH3O(44) <=> CO2(4) + CH3OH(14)\n", + " COOH(36) + CH2OH(47) <=> CO2(4) + CH3OH(14)\n", + " HCOO(38) + CH3O(44) <=> CO2(4) + CH3OH(14)\n", + " HCOO(38) + CH2OH(47) <=> CO2(4) + CH3OH(14)\n", + " CO(35) + COOH(36) <=> CO2(4) + HCO(15)\n", + " CO(35) + HCOO(38) <=> CO2(4) + HCO(15)\n", + " CO2(4) + HCO(15) <=> O=[C]OC=O(121)\n", + " CO2(4) + HCO(15) <=> [O]C(=O)C=O(122)\n", + " CO2(4) + CH3CHO(16) <=> O=CCOC=O(123)\n", + " CO2(4) + CH3CHO(16) <=> O=CCC(=O)O(124)\n", + " CC([O])C([O])=O(125) <=> CO2(4) + CH3CHO(16)\n", + " C[CH]OC([O])=O(126) <=> CO2(4) + CH3CHO(16)\n", + " CC([O])O[C]=O(127) <=> CO2(4) + CH3CHO(16)\n", + " C[CH]OO[C]=O(128) <=> CO2(4) + CH3CHO(16)\n", + " CO2(4) + CH3CHO(16) <=> CC1OOC1=O(129)\n", + " COOH(36) + [CH2]C=O(58) <=> CO2(4) + CH3CHO(16)\n", + " COOH(36) + C[C]=O(59) <=> CO2(4) + CH3CHO(16)\n", + " HCOO(38) + [CH2]C=O(58) <=> CO2(4) + CH3CHO(16)\n", + " HCOO(38) + C[C]=O(59) <=> CO2(4) + CH3CHO(16)\n", + " C=COOC=O(130) <=> CO2(4) + CH3CHO(16)\n", + " C=COC(=O)O(131) <=> CO2(4) + CH3CHO(16)\n", + " O(34) + COOH(36) <=> OH(17) + CO2(4)\n", + " O(34) + HCOO(38) <=> OH(17) + CO2(4)\n", + " OH(17) + CO2(4) <=> [O]C(=O)O(132)\n", + " CO2(4) + C2H4(18) <=> C=COC=O(133)\n", + " CO2(4) + C2H4(18) <=> C=CC(=O)O(134)\n", + " [CH2]CC([O])=O(135) <=> CO2(4) + C2H4(18)\n", + " [CH2]CO[C]=O(136) <=> CO2(4) + C2H4(18)\n", + " CO2(4) + C2H4(18) <=> O=C1CCO1(137)\n", + " COOH(36) + [CH]=C(60) <=> CO2(4) + C2H4(18)\n", + " HCOO(38) + [CH]=C(60) <=> CO2(4) + C2H4(18)\n", + " CO2(4) + CH3CH(19) <=> [CH]COC=O(138)\n", + " CO2(4) + CH3CH(19) <=> [CH]CC(=O)O(139)\n", + " COOH(36) + [CH][CH2](62) <=> CO2(4) + CH3CH(19)\n", + " COOH(36) + [C]C(140) <=> CO2(4) + CH3CH(19)\n", + " HCOO(38) + [CH][CH2](62) <=> CO2(4) + CH3CH(19)\n", + " HCOO(38) + [C]C(140) <=> CO2(4) + CH3CH(19)\n", + " CO2(4) + CH3CH(19) <=> C[CH]O[C]=O(141)\n", + " CO2(4) + CH3CH(19) <=> CC=C([O])[O](142)\n", + " CO2(4) + CH3OO(20) <=> [O]OCOC=O(143)\n", + " CO2(4) + CH3OO(20) <=> [O]OCC(=O)O(144)\n", + " COOH(36) + [CH2]O[O](63) <=> CO2(4) + CH3OO(20)\n", + " HCOO(38) + [CH2]O[O](63) <=> CO2(4) + CH3OO(20)\n", + " CO2(4) + CH3OO(20) <=> COOO[C]=O(145)\n", + " CO2(4) + CH3OO(20) <=> COOC([O])=O(146)\n", + " H2O(5) + CO(7) <=> O=CO(98)\n", + " OH(17) + CH2OH(47) <=> H2O(5) + CH2O(9)\n", + " OH(17) + CH3O(44) <=> H2O(5) + CH2O(9)\n", + " OH(17) + [CH]O(78) <=> H2O(5) + HCO(15)\n", + " OH(17) + C[CH]O(79) <=> H2O(5) + CH3CHO(16)\n", + " OH(17) + CC[O](80) <=> H2O(5) + CH3CHO(16)\n", + " H2O(5) + C2H4(18) <=> CCO(147)\n", + " OH(17) + COO(81) <=> H2O(5) + CH3OO(20)\n", + " H(12) + CH2OH(47) <=> H2(6) + CH2O(9)\n", + " H(12) + CH3O(44) <=> H2(6) + CH2O(9)\n", + " H(12) + [CH]O(78) <=> H2(6) + HCO(15)\n", + " H(12) + C[CH]O(79) <=> H2(6) + CH3CHO(16)\n", + " H(12) + CC[O](80) <=> H2(6) + CH3CHO(16)\n", + " H(12) + COO(81) <=> H2(6) + CH3OO(20)\n", + " CO(7) + C2H6(8) <=> CCC=O(148)\n", + " CO(7) + C2H6(8) <=> CC(C)=O(149)\n", + " CO(7) + CH2O(9) <=> O=CC=O(150)\n", + " HCO(15) + CH2(42) <=> CO(7) + CH3(10)\n", + " CO(7) + CH3(10) <=> C[C]=O(59)\n", + " CO(7) + C3H8(11) <=> CC(C)C=O(151)\n", + " CO(7) + C3H8(11) <=> CCCC=O(152)\n", + " CO(7) + C3H8(11) <=> CCC(C)=O(153)\n", + " HCO(15) + C[CH]C(53) <=> CO(7) + C3H8(11)\n", + " HCO(15) + [CH2]CC(54) <=> CO(7) + C3H8(11)\n", + " CO(7) + C2H5(13) <=> [CH2]CC=O(154)\n", + " HCO(15) + [CH2][CH2](55) <=> CO(7) + C2H5(13)\n", + " CC[C]=O(155) <=> CO(7) + C2H5(13)\n", + " CO(7) + CH3OH(14) <=> COC=O(76)\n", + " CO(7) + CH3OH(14) <=> O=CCO(156)\n", + " HCO(15) + CH3O(44) <=> CO(7) + CH3OH(14)\n", + " HCO(15) + CH2OH(47) <=> CO(7) + CH3OH(14)\n", + " CO(35) + HCO(15) <=> CO(7) + HCO(15)\n", + " CO(7) + HCO(15) <=> O=[C]C=O(157)\n", + " CO(7) + CH3CHO(16) <=> O=CCC=O(158)\n", + " CO(7) + CH3CHO(16) <=> CC(=O)C=O(159)\n", + " HCO(15) + [CH2]C=O(58) <=> CO(7) + CH3CHO(16)\n", + " HCO(15) + C[C]=O(59) <=> CO(7) + CH3CHO(16)\n", + " [CH-]=[O+]OC=C(160) <=> CO(7) + CH3CHO(16)\n", + " C=C[O+]=[C-]O(161) <=> CO(7) + CH3CHO(16)\n", + " O(34) + HCO(15) <=> OH(17) + CO(7)\n", + " OH(17) + CO(7) <=> COOH(36)\n", + " CO(7) + C2H4(18) <=> C=CC=O(162)\n", + " HCO(15) + [CH]=C(60) <=> CO(7) + C2H4(18)\n", + " CO(7) + CH3CH(19) <=> [CH]CC=O(163)\n", + " HCO(15) + [CH][CH2](62) <=> CO(7) + CH3CH(19)\n", + " HCO(15) + [C]C(140) <=> CO(7) + CH3CH(19)\n", + " CO(7) + CH3OO(20) <=> [O]OCC=O(164)\n", + " HCO(15) + [CH2]O[O](63) <=> CO(7) + CH3OO(20)\n", + " CO(7) + CH3OO(20) <=> COO[C]=O(165)\n", + " CH2OH(47) + C2H5(13) <=> CH2O(9) + C2H6(8)\n", + " CH3O(44) + C2H5(13) <=> CH2O(9) + C2H6(8)\n", + " [CH]O(78) + C2H5(13) <=> HCO(15) + C2H6(8)\n", + " C2H5(13) + C[CH]O(79) <=> CH3CHO(16) + C2H6(8)\n", + " C2H5(13) + CC[O](80) <=> CH3CHO(16) + C2H6(8)\n", + " CH3OO(20) + C2H6(8) <=> COO(81) + C2H5(13)\n", + " [O]CC[O](166) <=> CH2O(9) + CH2O(9)\n", + " [CH2]OC[O](167) <=> CH2O(9) + CH2O(9)\n", + " [CH2]OO[CH2](168) <=> CH2O(9) + CH2O(9)\n", + " CH2O(9) + CH2O(9) <=> C1COO1(169)\n", + " HCO(15) + CH2OH(47) <=> CH2O(9) + CH2O(9)\n", + " HCO(15) + CH3O(44) <=> CH2O(9) + CH2O(9)\n", + " CH2(42) + CH2OH(47) <=> CH2O(9) + CH3(10)\n", + " CH2(42) + CH3O(44) <=> CH2O(9) + CH3(10)\n", + " CH2O(9) + CH3(10) <=> [CH2]OC(170)\n", + " CH2O(9) + CH3(10) <=> CC[O](80)\n", + " CH2OH(47) + C[CH]C(53) <=> CH2O(9) + C3H8(11)\n", + " CH2OH(47) + [CH2]CC(54) <=> CH2O(9) + C3H8(11)\n", + " CH3O(44) + C[CH]C(53) <=> CH2O(9) + C3H8(11)\n", + " CH3O(44) + [CH2]CC(54) <=> CH2O(9) + C3H8(11)\n", + " H(12) + CH2O(9) <=> CH2OH(47)\n", + " H(12) + CH2O(9) <=> CH3O(44)\n", + " CH2OH(47) + [CH2][CH2](55) <=> CH2O(9) + C2H5(13)\n", + " CH2OH(47) + CH3CH(19) <=> CH2O(9) + C2H5(13)\n", + " CH3O(44) + [CH2][CH2](55) <=> CH2O(9) + C2H5(13)\n", + " CH3O(44) + CH3CH(19) <=> CH2O(9) + C2H5(13)\n", + " CH2O(9) + C2H5(13) <=> [CH2]OCC(171)\n", + " CH2O(9) + C2H5(13) <=> CCC[O](172)\n", + " CH2OH(47) + CH3O(44) <=> CH2O(9) + CH3OH(14)\n", + " CH2OH(47) + CH2OH(47) <=> CH2O(9) + CH3OH(14)\n", + " CH3O(44) + CH3O(44) <=> CH2O(9) + CH3OH(14)\n", + " CH2OH(47) + CH3O(44) <=> CH2O(9) + CH3OH(14)\n", + " HCO(15) + [CH]O(78) <=> HCO(15) + CH2O(9)\n", + " CO(35) + CH2OH(47) <=> HCO(15) + CH2O(9)\n", + " CO(35) + CH3O(44) <=> HCO(15) + CH2O(9)\n", + " HCO(15) + CH2O(9) <=> [CH2]OC=O(173)\n", + " HCO(15) + CH2O(9) <=> [O]CC=O(174)\n", + " CC([O])C[O](175) <=> CH2O(9) + CH3CHO(16)\n", + " C[CH]OC[O](176) <=> CH2O(9) + CH3CHO(16)\n", + " [CH2]OC(C)[O](177) <=> CH2O(9) + CH3CHO(16)\n", + " [CH2]OO[CH]C(178) <=> CH2O(9) + CH3CHO(16)\n", + " CH2O(9) + CH3CHO(16) <=> CC1COO1(179)\n", + " HCO(15) + C[CH]O(79) <=> CH2O(9) + CH3CHO(16)\n", + " HCO(15) + CC[O](80) <=> CH2O(9) + CH3CHO(16)\n", + " CH2OH(47) + [CH2]C=O(58) <=> CH2O(9) + CH3CHO(16)\n", + " CH2OH(47) + C[C]=O(59) <=> CH2O(9) + CH3CHO(16)\n", + " CH3O(44) + [CH2]C=O(58) <=> CH2O(9) + CH3CHO(16)\n", + " CH3O(44) + C[C]=O(59) <=> CH2O(9) + CH3CHO(16)\n", + " C=COOC(180) <=> CH2O(9) + CH3CHO(16)\n", + " C=COCO(181) <=> CH2O(9) + CH3CHO(16)\n", + " O(34) + CH2OH(47) <=> OH(17) + CH2O(9)\n", + " O(34) + CH3O(44) <=> OH(17) + CH2O(9)\n", + " OH(17) + CH2O(9) <=> [O]CO(182)\n", + " [CH2]CC[O](183) <=> CH2O(9) + C2H4(18)\n", + " [CH2]CO[CH2](184) <=> CH2O(9) + C2H4(18)\n", + " CH2O(9) + C2H4(18) <=> C1COC1(185)\n", + " CH2OH(47) + [CH]=C(60) <=> CH2O(9) + C2H4(18)\n", + " CH3O(44) + [CH]=C(60) <=> CH2O(9) + C2H4(18)\n", + " CH2OH(47) + [CH][CH2](62) <=> CH2O(9) + CH3CH(19)\n", + " CH2OH(47) + [C]C(140) <=> CH2O(9) + CH3CH(19)\n", + " CH3O(44) + [CH][CH2](62) <=> CH2O(9) + CH3CH(19)\n", + " CH3O(44) + [C]C(140) <=> CH2O(9) + CH3CH(19)\n", + " CH2O(9) + CH3CH(19) <=> [CH2]O[CH]C(186)\n", + " CH2O(9) + CH3CH(19) <=> C[CH]C[O](187)\n", + " [CH2]O[O](63) + CH2OH(47) <=> CH2O(9) + CH3OO(20)\n", + " [CH2]O[O](63) + CH3O(44) <=> CH2O(9) + CH3OO(20)\n", + " HCO(15) + COO(81) <=> CH2O(9) + CH3OO(20)\n", + " CH2O(9) + CH3OO(20) <=> [CH2]OOOC(188)\n", + " CH2O(9) + CH3OO(20) <=> COOC[O](189)\n", + " CH2(42) + CH4(2) <=> CH3(10) + CH3(10)\n", + " CH4(2) + C[CH]C(53) <=> CH3(10) + C3H8(11)\n", + " CH3(10) + C3H8(11) <=> CH4(2) + [CH2]CC(54)\n", + " H2(6) + CH2(42) <=> H(12) + CH3(10)\n", + " CH2(42) + C2H6(8) <=> CH3(10) + C2H5(13)\n", + " CH3O(44) + CH4(2) <=> CH3(10) + CH3OH(14)\n", + " CH3(10) + CH3OH(14) <=> CH2OH(47) + CH4(2)\n", + " CH2(42) + [CH]O(78) <=> HCO(15) + CH3(10)\n", + " CH2(42) + CH2O(9) <=> HCO(15) + CH3(10)\n", + " CO(35) + CH4(2) <=> HCO(15) + CH3(10)\n", + " CH2(42) + C[CH]O(79) <=> CH3(10) + CH3CHO(16)\n", + " CH2(42) + CC[O](80) <=> CH3(10) + CH3CHO(16)\n", + " CH3(10) + CH3CHO(16) <=> CH4(2) + [CH2]C=O(58)\n", + " CH3(10) + CH3CHO(16) <=> CH4(2) + C[C]=O(59)\n", + " CH3(10) + CH3CHO(16) <=> C[CH]OC(190)\n", + " CH3(10) + CH3CHO(16) <=> CC(C)[O](191)\n", + " OH(17) + CH3(10) <=> H2O(5) + CH2(42)\n", + " O(34) + CH4(2) <=> OH(17) + CH3(10)\n", + " CH2(42) + C2H5(13) <=> CH3(10) + C2H4(18)\n", + " CH4(2) + [CH]=C(60) <=> CH3(10) + C2H4(18)\n", + " CH3(10) + C2H4(18) <=> [CH2]CC(54)\n", + " CH3(10) + CH3CH(19) <=> C[CH]C(53)\n", + " CH3(10) + CH3CH(19) <=> CH4(2) + [CH]=C(60)\n", + " CH2(42) + C2H5(13) <=> CH3(10) + CH3CH(19)\n", + " CH3(10) + CH3CH(19) <=> CH4(2) + [CH][CH2](62)\n", + " CH2(42) + COO(81) <=> CH3(10) + CH3OO(20)\n", + " CH3(10) + CH3OO(20) <=> [CH2]O[O](63) + CH4(2)\n", + " CH3(10) + CH3OO(20) <=> COOC(192)\n", + " H2(6) + C[CH]C(53) <=> H(12) + C3H8(11)\n", + " H(12) + C3H8(11) <=> H2(6) + [CH2]CC(54)\n", + " C2H6(8) + C[CH]C(53) <=> C2H5(13) + C3H8(11)\n", + " C2H6(8) + [CH2]CC(54) <=> C2H5(13) + C3H8(11)\n", + " [CH]O(78) + C[CH]C(53) <=> HCO(15) + C3H8(11)\n", + " [CH]O(78) + [CH2]CC(54) <=> HCO(15) + C3H8(11)\n", + " HCO(15) + C3H8(11) <=> CH2O(9) + C[CH]C(53)\n", + " CH2O(9) + [CH2]CC(54) <=> HCO(15) + C3H8(11)\n", + " C[CH]O(79) + C[CH]C(53) <=> CH3CHO(16) + C3H8(11)\n", + " CC[O](80) + C[CH]C(53) <=> CH3CHO(16) + C3H8(11)\n", + " C[CH]O(79) + [CH2]CC(54) <=> CH3CHO(16) + C3H8(11)\n", + " CC[O](80) + [CH2]CC(54) <=> CH3CHO(16) + C3H8(11)\n", + " OH(17) + C3H8(11) <=> H2O(5) + C[CH]C(53)\n", + " OH(17) + C3H8(11) <=> H2O(5) + [CH2]CC(54)\n", + " C2H5(13) + C[CH]C(53) <=> C2H4(18) + C3H8(11)\n", + " C2H5(13) + [CH2]CC(54) <=> C2H4(18) + C3H8(11)\n", + " CH3CH(19) + C3H8(11) <=> C2H5(13) + C[CH]C(53)\n", + " CH3CH(19) + C3H8(11) <=> C2H5(13) + [CH2]CC(54)\n", + " COO(81) + C[CH]C(53) <=> CH3OO(20) + C3H8(11)\n", + " COO(81) + [CH2]CC(54) <=> CH3OO(20) + C3H8(11)\n", + " H(12) + CH3OH(14) <=> H2(6) + CH3O(44)\n", + " H(12) + CH3OH(14) <=> H2(6) + CH2OH(47)\n", + " H2(6) + CO(35) <=> H(12) + HCO(15)\n", + " H(12) + CH3CHO(16) <=> H2(6) + [CH2]C=O(58)\n", + " H(12) + CH3CHO(16) <=> H2(6) + C[C]=O(59)\n", + " H(12) + CH3CHO(16) <=> C[CH]O(79)\n", + " H(12) + CH3CHO(16) <=> CC[O](80)\n", + " H(12) + C2H4(18) <=> H2(6) + [CH]=C(60)\n", + " H(12) + CH3CH(19) <=> H2(6) + [CH]=C(60)\n", + " H(12) + CH3CH(19) <=> H2(6) + [CH][CH2](62)\n", + " H(12) + CH3OO(20) <=> H2(6) + [CH2]O[O](63)\n", + " H(12) + CH3OO(20) <=> COO(81)\n", + " C2H5(13) + C2H5(13) <=> CCCC(193)\n", + " CH3OH(14) + C2H5(13) <=> CH3O(44) + C2H6(8)\n", + " CH3OH(14) + C2H5(13) <=> CH2OH(47) + C2H6(8)\n", + " [CH]O(78) + [CH2][CH2](55) <=> HCO(15) + C2H5(13)\n", + " [CH]O(78) + CH3CH(19) <=> HCO(15) + C2H5(13)\n", + " CO(35) + C2H6(8) <=> HCO(15) + C2H5(13)\n", + " HCO(15) + C2H5(13) <=> CCC=O(148)\n", + " [CH2][CH2](55) + C[CH]O(79) <=> CH3CHO(16) + C2H5(13)\n", + " [CH2][CH2](55) + CC[O](80) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CH(19) + C[CH]O(79) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CH(19) + CC[O](80) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CHO(16) + C2H5(13) <=> [CH2]C=O(58) + C2H6(8)\n", + " C[C]=O(59) + C2H6(8) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CHO(16) + C2H5(13) <=> C[CH]OCC(194)\n", + " CH3CHO(16) + C2H5(13) <=> CCC(C)[O](195)\n", + " O(34) + C2H6(8) <=> OH(17) + C2H5(13)\n", + " OH(17) + C2H5(13) <=> CCO(147)\n", + " [CH2][CH2](55) + C2H5(13) <=> C2H4(18) + C2H5(13)\n", + " [CH]=C(60) + C2H6(8) <=> C2H4(18) + C2H5(13)\n", + " C2H4(18) + C2H5(13) <=> [CH2]CCC(196)\n", + " CH3CH(19) + C2H5(13) <=> C[CH]CC(197)\n", + " CH3CH(19) + C2H5(13) <=> [CH]=C(60) + C2H6(8)\n", + " [CH][CH2](62) + C2H6(8) <=> CH3CH(19) + C2H5(13)\n", + " CH3OO(20) + C2H5(13) <=> COO(81) + C2H4(18)\n", + " COO(81) + CH3CH(19) <=> CH3OO(20) + C2H5(13)\n", + " [CH2]O[O](63) + C2H6(8) <=> CH3OO(20) + C2H5(13)\n", + " CH3OO(20) + C2H5(13) <=> CCOOC(198)\n", + " [CH]O(78) + CH3O(44) <=> HCO(15) + CH3OH(14)\n", + " [CH]O(78) + CH2OH(47) <=> HCO(15) + CH3OH(14)\n", + " CH2O(9) + CH3O(44) <=> HCO(15) + CH3OH(14)\n", + " CH2O(9) + CH2OH(47) <=> HCO(15) + CH3OH(14)\n", + " CH3O(44) + C[CH]O(79) <=> CH3OH(14) + CH3CHO(16)\n", + " CH3O(44) + CC[O](80) <=> CH3OH(14) + CH3CHO(16)\n", + " CH2OH(47) + C[CH]O(79) <=> CH3OH(14) + CH3CHO(16)\n", + " CH2OH(47) + CC[O](80) <=> CH3OH(14) + CH3CHO(16)\n", + " OH(17) + CH3OH(14) <=> H2O(5) + CH3O(44)\n", + " OH(17) + CH3OH(14) <=> H2O(5) + CH2OH(47)\n", + " CH3OH(14) + C2H4(18) <=> CCOC(199)\n", + " CH3OH(14) + C2H4(18) <=> CCCO(200)\n", + " CH3O(44) + C2H5(13) <=> CH3OH(14) + C2H4(18)\n", + " CH2OH(47) + C2H5(13) <=> CH3OH(14) + C2H4(18)\n", + " CH3OH(14) + CH3CH(19) <=> CH3O(44) + C2H5(13)\n", + " CH3OH(14) + CH3CH(19) <=> CH2OH(47) + C2H5(13)\n", + " CH3O(44) + COO(81) <=> CH3OO(20) + CH3OH(14)\n", + " CH2OH(47) + COO(81) <=> CH3OO(20) + CH3OH(14)\n", + " CO(35) + [CH]O(78) <=> HCO(15) + HCO(15)\n", + " CO(35) + CH2O(9) <=> HCO(15) + HCO(15)\n", + " HCO(15) + HCO(15) <=> O=CC=O(150)\n", + " CO(35) + C[CH]O(79) <=> HCO(15) + CH3CHO(16)\n", + " CO(35) + CC[O](80) <=> HCO(15) + CH3CHO(16)\n", + " [CH]O(78) + [CH2]C=O(58) <=> HCO(15) + CH3CHO(16)\n", + " [CH]O(78) + C[C]=O(59) <=> HCO(15) + CH3CHO(16)\n", + " CH2O(9) + [CH2]C=O(58) <=> HCO(15) + CH3CHO(16)\n", + " CH2O(9) + C[C]=O(59) <=> HCO(15) + CH3CHO(16)\n", + " HCO(15) + CH3CHO(16) <=> C[CH]OC=O(201)\n", + " HCO(15) + CH3CHO(16) <=> CC([O])C=O(202)\n", + " O(34) + [CH]O(78) <=> OH(17) + HCO(15)\n", + " H2O(5) + CO(35) <=> OH(17) + HCO(15)\n", + " O(34) + CH2O(9) <=> OH(17) + HCO(15)\n", + " OH(17) + HCO(15) <=> O=CO(98)\n", + " CO(35) + C2H5(13) <=> HCO(15) + C2H4(18)\n", + " [CH]O(78) + [CH]=C(60) <=> HCO(15) + C2H4(18)\n", + " CH2O(9) + [CH]=C(60) <=> HCO(15) + C2H4(18)\n", + " HCO(15) + C2H4(18) <=> [CH2]CC=O(154)\n", + " HCO(15) + CH3CH(19) <=> C[CH]C=O(203)\n", + " HCO(15) + CH3CH(19) <=> CH2O(9) + [CH]=C(60)\n", + " [CH]O(78) + [CH][CH2](62) <=> HCO(15) + CH3CH(19)\n", + " [CH]O(78) + [C]C(140) <=> HCO(15) + CH3CH(19)\n", + " CO(35) + C2H5(13) <=> HCO(15) + CH3CH(19)\n", + " CH2O(9) + [CH][CH2](62) <=> HCO(15) + CH3CH(19)\n", + " HCO(15) + CH3OO(20) <=> CO(7) + COO(81)\n", + " [CH]O(78) + [CH2]O[O](63) <=> HCO(15) + CH3OO(20)\n", + " CO(35) + COO(81) <=> HCO(15) + CH3OO(20)\n", + " CH2O(9) + [CH2]O[O](63) <=> HCO(15) + CH3OO(20)\n", + " HCO(15) + CH3OO(20) <=> COOC=O(204)\n", + " CC([O])C(C)[O](205) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[CH]OC(C)[O](206) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[CH]OO[CH]C(207) <=> CH3CHO(16) + CH3CHO(16)\n", + " CH3CHO(16) + CH3CHO(16) <=> CC1OOC1C(208)\n", + " [CH2]C=O(58) + C[CH]O(79) <=> CH3CHO(16) + CH3CHO(16)\n", + " [CH2]C=O(58) + CC[O](80) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[C]=O(59) + C[CH]O(79) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[C]=O(59) + CC[O](80) <=> CH3CHO(16) + CH3CHO(16)\n", + " C=COOCC(209) <=> CH3CHO(16) + CH3CHO(16)\n", + " C=COC(C)O(210) <=> CH3CHO(16) + CH3CHO(16)\n", + " O(34) + C[CH]O(79) <=> OH(17) + CH3CHO(16)\n", + " O(34) + CC[O](80) <=> OH(17) + CH3CHO(16)\n", + " OH(17) + CH3CHO(16) <=> H2O(5) + [CH2]C=O(58)\n", + " OH(17) + CH3CHO(16) <=> H2O(5) + C[C]=O(59)\n", + " OH(17) + CH3CHO(16) <=> CC([O])O(211)\n", + " [CH2]CC(C)[O](212) <=> C2H4(18) + CH3CHO(16)\n", + " [CH2]CO[CH]C(213) <=> C2H4(18) + CH3CHO(16)\n", + " C2H4(18) + CH3CHO(16) <=> CC1CCO1(214)\n", + " [CH2]C=O(58) + C2H5(13) <=> C2H4(18) + CH3CHO(16)\n", + " C[C]=O(59) + C2H5(13) <=> C2H4(18) + CH3CHO(16)\n", + " [CH]=C(60) + C[CH]O(79) <=> C2H4(18) + CH3CHO(16)\n", + " [CH]=C(60) + CC[O](80) <=> C2H4(18) + CH3CHO(16)\n", + " C=COCC(215) <=> C2H4(18) + CH3CHO(16)\n", + " [CH][CH2](62) + C[CH]O(79) <=> CH3CH(19) + CH3CHO(16)\n", + " [C]C(140) + C[CH]O(79) <=> CH3CH(19) + CH3CHO(16)\n", + " [CH][CH2](62) + CC[O](80) <=> CH3CH(19) + CH3CHO(16)\n", + " [C]C(140) + CC[O](80) <=> CH3CH(19) + CH3CHO(16)\n", + " CH3CH(19) + CH3CHO(16) <=> [CH2]C=O(58) + C2H5(13)\n", + " CH3CH(19) + CH3CHO(16) <=> C[C]=O(59) + C2H5(13)\n", + " CH3CH(19) + CH3CHO(16) <=> C[CH]O[CH]C(216)\n", + " CH3CH(19) + CH3CHO(16) <=> C[CH]C(C)[O](217)\n", + " [CH2]O[O](63) + C[CH]O(79) <=> CH3OO(20) + CH3CHO(16)\n", + " [CH2]O[O](63) + CC[O](80) <=> CH3OO(20) + CH3CHO(16)\n", + " COO(81) + [CH2]C=O(58) <=> CH3OO(20) + CH3CHO(16)\n", + " COO(81) + C[C]=O(59) <=> CH3OO(20) + CH3CHO(16)\n", + " CH3OO(20) + CH3CHO(16) <=> C[CH]OOOC(218)\n", + " CH3OO(20) + CH3CHO(16) <=> COOC(C)[O](219)\n", + " O(34) + C2H5(13) <=> OH(17) + C2H4(18)\n", + " OH(17) + C2H4(18) <=> H2O(5) + [CH]=C(60)\n", + " OH(17) + C2H4(18) <=> [CH2]CO(220)\n", + " OH(17) + CH3CH(19) <=> C[CH]O(79)\n", + " OH(17) + CH3CH(19) <=> H2O(5) + [CH]=C(60)\n", + " OH(17) + CH3CH(19) <=> O(34) + C2H5(13)\n", + " OH(17) + CH3CH(19) <=> H2O(5) + [CH][CH2](62)\n", + " O(34) + COO(81) <=> OH(17) + CH3OO(20)\n", + " OH(17) + CH3OO(20) <=> H2O(5) + [CH2]O[O](63)\n", + " OH(17) + CH3OO(20) <=> COOO(221)\n", + " [CH2]CC[CH2](222) <=> C2H4(18) + C2H4(18)\n", + " [CH]=C(60) + C2H5(13) <=> C2H4(18) + C2H4(18)\n", + " [CH][CH2](62) + C2H5(13) <=> C2H4(18) + CH3CH(19)\n", + " [C]C(140) + C2H5(13) <=> C2H4(18) + CH3CH(19)\n", + " [CH]=C(60) + C2H5(13) <=> C2H4(18) + CH3CH(19)\n", + " C2H4(18) + CH3CH(19) <=> [CH2]C[CH]C(223)\n", + " [CH2]O[O](63) + C2H5(13) <=> CH3OO(20) + C2H4(18)\n", + " COO(81) + [CH]=C(60) <=> CH3OO(20) + C2H4(18)\n", + " CH3OO(20) + C2H4(18) <=> [CH2]COOC(224)\n", + " CH3CH(19) + CH3CH(19) <=> [CH]=C(60) + C2H5(13)\n", + " CH3CH(19) + CH3CH(19) <=> [CH][CH2](62) + C2H5(13)\n", + " CH3OO(20) + CH3CH(19) <=> C[CH]OOC(225)\n", + " CH3OO(20) + CH3CH(19) <=> COO(81) + [CH]=C(60)\n", + " COO(81) + [CH][CH2](62) <=> CH3OO(20) + CH3CH(19)\n", + " CH3OO(20) + CH3CH(19) <=> [CH2]O[O](63) + C2H5(13)\n", + " [CH2]O[O](63) + COO(81) <=> CH3OO(20) + CH3OO(20)\n", + " CH3OO(20) + CH3OO(20) <=> COOOOC(226)\n", + " HX(21) + CO2X(22) <=> X(1) + O=C[O][Pt](227)\n", + " X(1) + O[C]#[Pt](228) <=> HX(21) + COX(23)\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + CHOX(33)\n", + " O=C[O][Pt](227) + CH3X(27) <=> CO2X(22) + CH4X(24)\n", + " COOH_X(37) + CH3X(27) <=> CO2X(22) + CH4X(24)\n", + " O=C[O][Pt](227) + CH2X(26) <=> CO2X(22) + CH3X(27)\n", + " COOH_X(37) + CH2X(26) <=> CO2X(22) + CH3X(27)\n", + " X(1) + CC(=O)[O][Pt](230) <=> CO2X(22) + CH3X(27)\n", + " X(1) + CO[C](=O)[Pt](231) <=> CO2X(22) + CH3X(27)\n", + " OX(25) + O=C[O][Pt](227) <=> OHX(31) + CO2X(22)\n", + " X(1) + O=C(O)[O][Pt](232) <=> OHX(31) + CO2X(22)\n", + " X(1) + O=[C]([Pt])OO(233) <=> OHX(31) + CO2X(22)\n", + " OHX(31) + O=C[O][Pt](227) <=> H2OX(32) + CO2X(22)\n", + " COX(23) + O=C[O][Pt](227) <=> CO2X(22) + CHOX(33)\n", + " COX(23) + COOH_X(37) <=> CO2X(22) + CHOX(33)\n", + " X(1) + O=CC(=O)[O][Pt](234) <=> CO2X(22) + CHOX(33)\n", + " X(1) + O=CO[C](=O)[Pt](235) <=> CO2X(22) + CHOX(33)\n", + " X(1) + X(1) + O=C=C=O(236) <=> COX(23) + COX(23)\n", + " X(1) + O=C=C=O.[Pt](237) <=> COX(23) + COX(23)\n", + " COX(23) + CH4X(24) <=> HX(21) + C[C](=O)[Pt](70)\n", + " CHX(28) + O[C]#[Pt](228) <=> COX(23) + CH2X(26)\n", + " X(1) + X(1) + C=C=O(238) <=> COX(23) + CH2X(26)\n", + " X(1) + C=C=O.[Pt](239) <=> COX(23) + CH2X(26)\n", + " O[C]#[Pt](228) + CH2X(26) <=> COX(23) + CH3X(27)\n", + " X(1) + C[C](=O)[Pt](70) <=> COX(23) + CH3X(27)\n", + " CX(29) + O[C]#[Pt](228) <=> COX(23) + CHX(28)\n", + " X(1) + O=C=[CH][Pt](240) <=> COX(23) + CHX(28)\n", + " X(1) + O=C=[C]=[Pt](241) <=> CX(29) + COX(23)\n", + " OX(25) + O[C]#[Pt](228) <=> OHX(31) + COX(23)\n", + " X(1) + OO[C]#[Pt](242) <=> OHX(31) + COX(23)\n", + " COX(23) + O[C]#[Pt](228) <=> COX(23) + CHOX(33)\n", + " X(1) + O=C[C](=O)[Pt](243) <=> COX(23) + CHOX(33)\n", + " OX(25) + CH4X(24) <=> HX(21) + CH3O_X(45)\n", + " CH2X(26) + CH4X(24) <=> HX(21) + C[CH2][Pt](64)\n", + " CHX(28) + CH4X(24) <=> HX(21) + C[CH]=[Pt](71)\n", + " CX(29) + CH4X(24) <=> HX(21) + C[C]#[Pt](244)\n", + " CH2O_X(46) + CH3X(27) <=> CHOX(33) + CH4X(24)\n", + " X(1) + O=O.[Pt](245) <=> OX(25) + OX(25)\n", + " X(1) + CH2O_X(46) <=> OX(25) + CH2X(26)\n", + " X(1) + CH3O_X(45) <=> OX(25) + CH3X(27)\n", + " X(1) + O[O][Pt](246) <=> OX(25) + OHX(31)\n", + " OX(25) + H2OX(32) <=> HX(21) + O[O][Pt](246)\n", + " X(1) + O=C[O][Pt](227) <=> OX(25) + CHOX(33)\n", + " X(1) + C=C.[Pt](74) <=> CH2X(26) + CH2X(26)\n", + " X(1) + C[CH2][Pt](64) <=> CH2X(26) + CH3X(27)\n", + " X(1) + C=[CH][Pt](73) <=> CHX(28) + CH2X(26)\n", + " X(1) + C=[C]=[Pt](247) <=> CX(29) + CH2X(26)\n", + " X(1) + CH2OH_X(48) <=> OHX(31) + CH2X(26)\n", + " H2OX(32) + CH2X(26) <=> HX(21) + CH2OH_X(48)\n", + " CHX(28) + O[CH]=[Pt](229) <=> CHOX(33) + CH2X(26)\n", + " CHX(28) + CH2O_X(46) <=> CHOX(33) + CH2X(26)\n", + " X(1) + O=C[CH2][Pt](69) <=> CHOX(33) + CH2X(26)\n", + " X(1) + CC.[Pt](65) <=> CH3X(27) + CH3X(27)\n", + " X(1) + C[CH]=[Pt](71) <=> CHX(28) + CH3X(27)\n", + " X(1) + C[C]#[Pt](244) <=> CX(29) + CH3X(27)\n", + " X(1) + CH3OH_X(43) <=> OHX(31) + CH3X(27)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHOX(33) + CH3X(27)\n", + " CH2X(26) + CH2O_X(46) <=> CHOX(33) + CH3X(27)\n", + " X(1) + CC=O.[Pt](72) <=> CHOX(33) + CH3X(27)\n", + " X(1) + O[CH]=[Pt](229) <=> OHX(31) + CHX(28)\n", + " H2OX(32) + CHX(28) <=> HX(21) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + CHOX(33)\n", + " CX(29) + CH2O_X(46) <=> CHX(28) + CHOX(33)\n", + " X(1) + O=C[CH]=[Pt](248) <=> CHX(28) + CHOX(33)\n", + " X(1) + O[C]#[Pt](228) <=> OHX(31) + CX(29)\n", + " H2OX(32) + CX(29) <=> HX(21) + O[C]#[Pt](228)\n", + " X(1) + O=C[C]#[Pt](249) <=> CX(29) + CHOX(33)\n", + " X(1) + X(1) + H2O2(51) <=> OHX(31) + OHX(31)\n", + " OX(25) + O[CH]=[Pt](229) <=> OHX(31) + CHOX(33)\n", + " OX(25) + CH2O_X(46) <=> OHX(31) + CHOX(33)\n", + " X(1) + X(1) + O=CO(98) <=> OHX(31) + CHOX(33)\n", + " X(1) + OO[CH]=[Pt](250) <=> OHX(31) + CHOX(33)\n", + " X(1) + O=CO.[Pt](251) <=> OHX(31) + CHOX(33)\n", + " OHX(31) + CH2O_X(46) <=> H2OX(32) + CHOX(33)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHOX(33) + CHOX(33)\n", + " COX(23) + CH2O_X(46) <=> CHOX(33) + CHOX(33)\n", + " X(1) + X(1) + O=CC=O(150) <=> CHOX(33) + CHOX(33)\n", + " X(1) + O=CC=O.[Pt](252) <=> CHOX(33) + CHOX(33)\n", + "\n", + "After model enlargement:\n", + " The model core has 36 species and 109 reactions\n", + " The model edge has 219 species and 613 reactions\n", + "\n", + "\n", + "Completed initial enlarge edge step.\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 63 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 63 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 180 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 180 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:39\n", + " Memory used: 884.97 MB\n", + "\n", + "Making seed mechanism...\n", + "Beginning model generation stage 1...\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "Reached max number of objects...preparing to terminate\n", + "At time 4.6109e-09 s, species O[CH]=[Pt](229) at rate ratio 0.10030650099699316 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species O[CH]=[Pt](229) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " O[CH]=[Pt](229)\n", + "Created 0 new edge species\n", + "Moved 8 reactions from edge to core\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + CHOX(33)\n", + " CHX(28) + O[CH]=[Pt](229) <=> CHOX(33) + CH2X(26)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHOX(33) + CH3X(27)\n", + " X(1) + O[CH]=[Pt](229) <=> OHX(31) + CHX(28)\n", + " H2OX(32) + CHX(28) <=> HX(21) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + CHOX(33)\n", + " OX(25) + O[CH]=[Pt](229) <=> OHX(31) + CHOX(33)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHOX(33) + CHOX(33)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 37 species and 117 reactions\n", + " The model edge has 218 species and 605 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "For reaction OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229), Ea raised from 48.7 to 53.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229), Ea raised from 143.8 to 146.0 kJ/mol to match endothermicity of reaction.\n", + "For reaction X(1) + O[CH](O)[Pt](260) <=> OHX(31) + O[CH]=[Pt](229), Ea raised from 58.3 to 62.4 kJ/mol to match endothermicity of reaction.\n", + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 11 new edge species\n", + " O=C=CO(253)\n", + " O=C=CO.[Pt](254)\n", + " C[CH](O)[Pt](255)\n", + " C=CO.[Pt](256)\n", + " OC=[CH][Pt](257)\n", + " OC=[C]=[Pt](258)\n", + " OO.[Pt](259)\n", + " O[CH](O)[Pt](260)\n", + " O=C[CH](O)[Pt](261)\n", + " OC=CO(262)\n", + " OC=CO.[Pt](263)\n", + "Added 0 new core reactions\n", + "Created 48 new edge reactions\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + O[C]#[Pt](228)\n", + " H2X(30) + O[C]#[Pt](228) <=> HX(21) + O[CH]=[Pt](229)\n", + " X(1) + CH2OH_X(48) <=> HX(21) + O[CH]=[Pt](229)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHX(28) + COOH_X(37)\n", + " CHOX(33) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " CHX(28) + OO[C]#[Pt](242) <=> COX(23) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " X(1) + X(1) + O=C=CO(253) <=> COX(23) + O[CH]=[Pt](229)\n", + " X(1) + O=C=CO.[Pt](254) <=> COX(23) + O[CH]=[Pt](229)\n", + " O[CH]=[Pt](229) + CH4X(24) <=> CH3X(27) + CH2OH_X(48)\n", + " O[CH]=[Pt](229) + CH4X(24) <=> HX(21) + C[CH](O)[Pt](255)\n", + " O[O][Pt](246) + CHX(28) <=> OX(25) + O[CH]=[Pt](229)\n", + " OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229)\n", + " X(1) + X(1) + O=CO(98) <=> OX(25) + O[CH]=[Pt](229)\n", + " X(1) + O=CO.[Pt](251) <=> OX(25) + O[CH]=[Pt](229)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHX(28) + CH2OH_X(48)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> O[C]#[Pt](228) + CH3X(27)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHX(28) + CH2OH_X(48)\n", + " X(1) + X(1) + C=CO(57) <=> CH2X(26) + O[CH]=[Pt](229)\n", + " X(1) + C=CO.[Pt](256) <=> CH2X(26) + O[CH]=[Pt](229)\n", + " CH2X(26) + CH2OH_X(48) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " CHX(28) + CH3OH_X(43) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " O[C]#[Pt](228) + CH4X(24) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " X(1) + C[CH](O)[Pt](255) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " O[C]#[Pt](228) + CH2X(26) <=> CHX(28) + O[CH]=[Pt](229)\n", + " CX(29) + CH2OH_X(48) <=> CHX(28) + O[CH]=[Pt](229)\n", + " X(1) + OC=[CH][Pt](257) <=> CHX(28) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " X(1) + OC=[C]=[Pt](258) <=> CX(29) + O[CH]=[Pt](229)\n", + " H2X(30) + O[CH]=[Pt](229) <=> HX(21) + CH2OH_X(48)\n", + " OHX(31) + O[CH]=[Pt](229) <=> OX(25) + CH2OH_X(48)\n", + " OO.[Pt](259) + CHX(28) <=> OHX(31) + O[CH]=[Pt](229)\n", + " H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229)\n", + " X(1) + O[CH](O)[Pt](260) <=> OHX(31) + O[CH]=[Pt](229)\n", + " H2OX(32) + O[CH]=[Pt](229) <=> OHX(31) + CH2OH_X(48)\n", + " H2OX(32) + O[CH]=[Pt](229) <=> HX(21) + O[CH](O)[Pt](260)\n", + " CHOX(33) + O[CH]=[Pt](229) <=> COX(23) + CH2OH_X(48)\n", + " CHX(28) + OO[CH]=[Pt](250) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[CH]=[Pt](229) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " CHX(28) + O=CO.[Pt](251) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + CH2O_X(46) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " X(1) + O=C[CH](O)[Pt](261) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " O[CH]=[Pt](229) + O[CH]=[Pt](229) <=> CHX(28) + O[CH](O)[Pt](260)\n", + " O[CH]=[Pt](229) + O[CH]=[Pt](229) <=> O[C]#[Pt](228) + CH2OH_X(48)\n", + " O[CH]=[Pt](229) + O[CH]=[Pt](229) <=> CHOX(33) + CH2OH_X(48)\n", + " X(1) + X(1) + OC=CO(262) <=> O[CH]=[Pt](229) + O[CH]=[Pt](229)\n", + " X(1) + OC=CO.[Pt](263) <=> O[CH]=[Pt](229) + O[CH]=[Pt](229)\n", + "\n", + "After model enlargement:\n", + " The model core has 37 species and 117 reactions\n", + " The model edge has 229 species and 653 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 71 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 71 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 228 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 228 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:50\n", + " Memory used: 886.73 MB\n", + "\n", + "Conducting simulation of reaction system 2...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "Reached max number of objects...preparing to terminate\n", + "At time 2.3883e-09 s, species O[C]#[Pt](228) at rate ratio 0.10006247522197795 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species O[C]#[Pt](228) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " O[C]#[Pt](228)\n", + "Created 0 new edge species\n", + "Moved 20 reactions from edge to core\n", + " X(1) + O[C]#[Pt](228) <=> HX(21) + COX(23)\n", + " CHX(28) + O[C]#[Pt](228) <=> COX(23) + CH2X(26)\n", + " O[C]#[Pt](228) + CH2X(26) <=> COX(23) + CH3X(27)\n", + " CX(29) + O[C]#[Pt](228) <=> COX(23) + CHX(28)\n", + " OX(25) + O[C]#[Pt](228) <=> OHX(31) + COX(23)\n", + " COX(23) + O[C]#[Pt](228) <=> COX(23) + CHOX(33)\n", + " X(1) + O[C]#[Pt](228) <=> OHX(31) + CX(29)\n", + " H2OX(32) + CX(29) <=> HX(21) + O[C]#[Pt](228)\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + O[C]#[Pt](228)\n", + " H2X(30) + O[C]#[Pt](228) <=> HX(21) + O[CH]=[Pt](229)\n", + " CHOX(33) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> O[C]#[Pt](228) + CH3X(27)\n", + " O[C]#[Pt](228) + CH4X(24) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " O[C]#[Pt](228) + CH2X(26) <=> CHX(28) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[CH]=[Pt](229) <=> CHOX(33) + O[CH]=[Pt](229)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 38 species and 137 reactions\n", + " The model edge has 228 species and 633 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 6 new edge species\n", + " O=C=[C](O)[Pt](264)\n", + " C[C](O)=[Pt](265)\n", + " C=[C](O)[Pt](266)\n", + " O[C](O)=[Pt](267)\n", + " O=C[C](O)=[Pt](268)\n", + " OC=[C](O)[Pt](269)\n", + "Added 0 new core reactions\n", + "Created 21 new edge reactions\n", + " CX(29) + COOH_X(37) <=> COX(23) + O[C]#[Pt](228)\n", + " CX(29) + OO[C]#[Pt](242) <=> COX(23) + O[C]#[Pt](228)\n", + " X(1) + O=C=[C](O)[Pt](264) <=> COX(23) + O[C]#[Pt](228)\n", + " O[C]#[Pt](228) + CH4X(24) <=> HX(21) + C[C](O)=[Pt](265)\n", + " O[O][Pt](246) + CX(29) <=> OX(25) + O[C]#[Pt](228)\n", + " X(1) + COOH_X(37) <=> OX(25) + O[C]#[Pt](228)\n", + " CX(29) + CH2OH_X(48) <=> O[C]#[Pt](228) + CH2X(26)\n", + " X(1) + C=[C](O)[Pt](266) <=> O[C]#[Pt](228) + CH2X(26)\n", + " CX(29) + CH3OH_X(43) <=> O[C]#[Pt](228) + CH3X(27)\n", + " X(1) + C[C](O)=[Pt](265) <=> O[C]#[Pt](228) + CH3X(27)\n", + " OO.[Pt](259) + CX(29) <=> OHX(31) + O[C]#[Pt](228)\n", + " X(1) + O[C](O)=[Pt](267) <=> OHX(31) + O[C]#[Pt](228)\n", + " H2OX(32) + O[C]#[Pt](228) <=> HX(21) + O[C](O)=[Pt](267)\n", + " CX(29) + OO[CH]=[Pt](250) <=> CHOX(33) + O[C]#[Pt](228)\n", + " CX(29) + O=CO.[Pt](251) <=> CHOX(33) + O[C]#[Pt](228)\n", + " X(1) + O=C[C](O)=[Pt](268) <=> CHOX(33) + O[C]#[Pt](228)\n", + " CX(29) + O[CH](O)[Pt](260) <=> O[C]#[Pt](228) + O[CH]=[Pt](229)\n", + " CHX(28) + O[C](O)=[Pt](267) <=> O[C]#[Pt](228) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[CH]=[Pt](229) <=> COX(23) + CH2OH_X(48)\n", + " X(1) + OC=[C](O)[Pt](269) <=> O[C]#[Pt](228) + O[CH]=[Pt](229)\n", + " CX(29) + O[C](O)=[Pt](267) <=> O[C]#[Pt](228) + O[C]#[Pt](228)\n", + "\n", + "After model enlargement:\n", + " The model core has 38 species and 137 reactions\n", + " The model edge has 234 species and 654 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 91 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 91 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 249 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 249 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:01:03\n", + " Memory used: 886.74 MB\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "Reached max number of objects...preparing to terminate\n", + "At time 7.4162e-09 s, species COOH_X(37) at rate ratio 0.1003670787675018 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species COOH_X(37) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " COOH_X(37)\n", + "Created 0 new edge species\n", + "Moved 11 reactions from edge to core\n", + " X(1) + COOH_X(37) <=> OHX(31) + COX(23)\n", + " X(1) + COOH_X(37) <=> HX(21) + CO2X(22)\n", + " H2OX(32) + COX(23) <=> HX(21) + COOH_X(37)\n", + " OHX(31) + CO2X(22) <=> OX(25) + COOH_X(37)\n", + " H2OX(32) + CO2X(22) <=> OHX(31) + COOH_X(37)\n", + " COOH_X(37) + CH3X(27) <=> CO2X(22) + CH4X(24)\n", + " COOH_X(37) + CH2X(26) <=> CO2X(22) + CH3X(27)\n", + " COX(23) + COOH_X(37) <=> CO2X(22) + CHOX(33)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHX(28) + COOH_X(37)\n", + " CX(29) + COOH_X(37) <=> COX(23) + O[C]#[Pt](228)\n", + " X(1) + COOH_X(37) <=> OX(25) + O[C]#[Pt](228)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 39 species and 148 reactions\n", + " The model edge has 233 species and 643 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 16 new edge species\n", + " O=C(O)C(=O)[O][Pt](270)\n", + " O=C(O)O[C](=O)[Pt](271)\n", + " O=C(O)[C](=O)[Pt](272)\n", + " O=C(O)[CH2][Pt](273)\n", + " CC(=O)O.[Pt](274)\n", + " O=C(O)[CH]=[Pt](275)\n", + " O=C(O)[C]#[Pt](276)\n", + " O=C(O)O(277)\n", + " OO[C](O)=[Pt](278)\n", + " O=C(O)O.[Pt](279)\n", + " O=CC(=O)O(280)\n", + " O=CC(=O)O.[Pt](281)\n", + " O=C(O)[CH](O)[Pt](282)\n", + " O=C(O)[C](O)=[Pt](283)\n", + " O=C(O)C(=O)O(284)\n", + " O=C(O)C(=O)O.[Pt](285)\n", + "Added 0 new core reactions\n", + "Created 55 new edge reactions\n", + " X(1) + X(1) + O=CO(98) <=> HX(21) + COOH_X(37)\n", + " X(1) + O[C](O)=[Pt](267) <=> HX(21) + COOH_X(37)\n", + " HX(21) + COOH_X(37) <=> X(1) + O=CO.[Pt](251)\n", + " COX(23) + O=C(O)[O][Pt](232) <=> CO2X(22) + COOH_X(37)\n", + " COX(23) + O=[C]([Pt])OO(233) <=> CO2X(22) + COOH_X(37)\n", + " X(1) + O=C(O)C(=O)[O][Pt](270) <=> CO2X(22) + COOH_X(37)\n", + " X(1) + O=C(O)O[C](=O)[Pt](271) <=> CO2X(22) + COOH_X(37)\n", + " COX(23) + OO[C]#[Pt](242) <=> COX(23) + COOH_X(37)\n", + " X(1) + O=C(O)[C](=O)[Pt](272) <=> COX(23) + COOH_X(37)\n", + " O=CO.[Pt](251) + CH3X(27) <=> COOH_X(37) + CH4X(24)\n", + " O[O][Pt](246) + COX(23) <=> OX(25) + COOH_X(37)\n", + " X(1) + O=C(O)[O][Pt](232) <=> OX(25) + COOH_X(37)\n", + " COOH_X(37) + CH2X(26) <=> COX(23) + CH2OH_X(48)\n", + " CHX(28) + O[C](O)=[Pt](267) <=> COOH_X(37) + CH2X(26)\n", + " CHX(28) + O=CO.[Pt](251) <=> COOH_X(37) + CH2X(26)\n", + " X(1) + O=C(O)[CH2][Pt](273) <=> COOH_X(37) + CH2X(26)\n", + " CH2X(26) + O[C](O)=[Pt](267) <=> COOH_X(37) + CH3X(27)\n", + " COX(23) + CH3OH_X(43) <=> COOH_X(37) + CH3X(27)\n", + " CH2X(26) + O=CO.[Pt](251) <=> COOH_X(37) + CH3X(27)\n", + " X(1) + X(1) + CC(=O)O(77) <=> COOH_X(37) + CH3X(27)\n", + " X(1) + CC(=O)O.[Pt](274) <=> COOH_X(37) + CH3X(27)\n", + " CX(29) + O[C](O)=[Pt](267) <=> CHX(28) + COOH_X(37)\n", + " CX(29) + O=CO.[Pt](251) <=> CHX(28) + COOH_X(37)\n", + " X(1) + O=C(O)[CH]=[Pt](275) <=> CHX(28) + COOH_X(37)\n", + " X(1) + O=C(O)[C]#[Pt](276) <=> CX(29) + COOH_X(37)\n", + " OX(25) + O[C](O)=[Pt](267) <=> OHX(31) + COOH_X(37)\n", + " OO.[Pt](259) + COX(23) <=> OHX(31) + COOH_X(37)\n", + " OX(25) + O=CO.[Pt](251) <=> OHX(31) + COOH_X(37)\n", + " X(1) + X(1) + O=C(O)O(277) <=> OHX(31) + COOH_X(37)\n", + " X(1) + OO[C](O)=[Pt](278) <=> OHX(31) + COOH_X(37)\n", + " X(1) + O=C(O)O.[Pt](279) <=> OHX(31) + COOH_X(37)\n", + " OHX(31) + O=CO.[Pt](251) <=> H2OX(32) + COOH_X(37)\n", + " COX(23) + OO[CH]=[Pt](250) <=> CHOX(33) + COOH_X(37)\n", + " COX(23) + O[C](O)=[Pt](267) <=> CHOX(33) + COOH_X(37)\n", + " CHOX(33) + COOH_X(37) <=> CO2X(22) + CH2O_X(46)\n", + " COX(23) + O=CO.[Pt](251) <=> CHOX(33) + COOH_X(37)\n", + " COX(23) + O=CO.[Pt](251) <=> CHOX(33) + COOH_X(37)\n", + " X(1) + X(1) + O=CC(=O)O(280) <=> CHOX(33) + COOH_X(37)\n", + " X(1) + O=CC(=O)O.[Pt](281) <=> CHOX(33) + COOH_X(37)\n", + " COOH_X(37) + O[CH]=[Pt](229) <=> COX(23) + O[CH](O)[Pt](260)\n", + " CHX(28) + OO[C](O)=[Pt](278) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[C](O)=[Pt](267) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " COOH_X(37) + O[CH]=[Pt](229) <=> CO2X(22) + CH2OH_X(48)\n", + " CHX(28) + O=C(O)O.[Pt](279) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O=CO.[Pt](251) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " X(1) + O=C(O)[CH](O)[Pt](282) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " COX(23) + O[C](O)=[Pt](267) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " CX(29) + OO[C](O)=[Pt](278) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " CX(29) + O=C(O)O.[Pt](279) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " X(1) + O=C(O)[C](O)=[Pt](283) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " COX(23) + OO[C](O)=[Pt](278) <=> COOH_X(37) + COOH_X(37)\n", + " COOH_X(37) + COOH_X(37) <=> CO2X(22) + O=CO.[Pt](251)\n", + " COX(23) + O=C(O)O.[Pt](279) <=> COOH_X(37) + COOH_X(37)\n", + " X(1) + X(1) + O=C(O)C(=O)O(284) <=> COOH_X(37) + COOH_X(37)\n", + " X(1) + O=C(O)C(=O)O.[Pt](285) <=> COOH_X(37) + COOH_X(37)\n", + "\n", + "After model enlargement:\n", + " The model core has 39 species and 148 reactions\n", + " The model edge has 249 species and 698 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:01:21\n", + " Memory used: 886.74 MB\n", + "\n", + "Conducting simulation of reaction system 2...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "Reached max number of objects...preparing to terminate\n", + "At time 5.4841e-08 s, species [CH]C(61) at rate ratio 0.10142010078648805 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species [CH]C(61) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " [CH]C(61)\n", + "Created 0 new edge species\n", + "Moved 1 reactions from edge to core\n", + " [CH]C(61) <=> C2H4(18)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 40 species and 149 reactions\n", + " The model edge has 248 species and 697 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "Generating thermo for new species...\n", + "Warning: Marked reaction X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27) as duplicate of X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27) for saving to Chemkin file.\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 9 new edge species\n", + " [CH][CH2][Pt](286)\n", + " [CH]C.[Pt](287)\n", + " CC1OC1=O(288)\n", + " [CH]COC=O(289)\n", + " [CH]CC(=O)O(290)\n", + " [CH]CC=O(291)\n", + " CC1CO1(292)\n", + " CC1OC1C(293)\n", + " CC1CC1(294)\n", + "Added 3 new core reactions\n", + " X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27)\n", + " X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27)\n", + " X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27)\n", + "Created 11 new edge reactions\n", + " CH(41) + CH3(10) <=> [CH]C(61)\n", + " X(1) + X(1) + [CH]C(61) <=> HX(21) + [CH][CH2][Pt](286)\n", + " X(1) + X(1) + [CH]C(61) <=> HX(21) + C[C]#[Pt](244)\n", + " X(1) + [CH]C(61) <=> [CH]C.[Pt](287)\n", + " CO2(4) + [CH]C(61) <=> CC1OC1=O(288)\n", + " CO2(4) + [CH]C(61) <=> [CH]COC=O(289)\n", + " CO2(4) + [CH]C(61) <=> [CH]CC(=O)O(290)\n", + " CO(7) + [CH]C(61) <=> [CH]CC=O(291)\n", + " CH2O(9) + [CH]C(61) <=> CC1CO1(292)\n", + " [CH]C(61) + CH3CHO(16) <=> CC1OC1C(293)\n", + " C2H4(18) + [CH]C(61) <=> CC1CC1(294)\n", + "\n", + "After model enlargement:\n", + " The model core has 40 species and 152 reactions\n", + " The model edge has 257 species and 708 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 47 reactions.\n", + "Chemkin file contains 105 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 47 reactions.\n", + "Chemkin file contains 105 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 553 reactions.\n", + "Chemkin file contains 310 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 553 reactions.\n", + "Chemkin file contains 310 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:01:43\n", + " Memory used: 888.25 MB\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "At time 6.6507e-06 s, reached target termination RateRatio: 0.0497658467962582\n", + " CH4(2) conversion: 0.2206 \n", + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 40 species and 152 reactions\n", + " The model edge has 257 species and 708 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 47 reactions.\n", + "Chemkin file contains 105 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 47 reactions.\n", + "Chemkin file contains 105 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 553 reactions.\n", + "Chemkin file contains 310 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 553 reactions.\n", + "Chemkin file contains 310 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:02:32\n", + " Memory used: 894.27 MB\n", + "\n", + "Conducting simulation of reaction system 2...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "At time 4.7641e-06 s, reached target termination RateRatio: 0.04974422721809749\n", + " CH4(2) conversion: 0.8567 \n", + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 40 species and 152 reactions\n", + " The model edge has 257 species and 708 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 47 reactions.\n", + "Chemkin file contains 105 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 47 reactions.\n", + "Chemkin file contains 105 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 553 reactions.\n", + "Chemkin file contains 310 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 553 reactions.\n", + "Chemkin file contains 310 reactions.\n", + "Saving current model core to HTML file...\n", + "Saving current model edge to HTML file...\n", + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:03:08\n", + " Memory used: 897.90 MB\n", + "\n", + "Making seed mechanism...\n", + "Performing final model checks...\n", + "No collision rate violators found in the model's core.\n", + "\n", + "MODEL GENERATION COMPLETED\n", + "\n", + "The final model core has 40 species and 152 reactions\n", + "The final model edge has 257 species and 708 reactions\n", + "\n", + "RMG execution terminated at Tue Apr 7 16:48:07 2026\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_rmg_run_dir = os.path.join('temp', 'base_rmg_run')\n", + "cov_dep_rmg_run_dir = os.path.join('temp', 'cov_dep_rmg_run')\n", + "os.makedirs(base_rmg_run_dir, exist_ok=True)\n", + "os.makedirs(cov_dep_rmg_run_dir, exist_ok=True)\n", + "\n", + "input_file = \"\"\"\n", + "# Data sources\n", + "database(\n", + " thermoLibraries=['covDepSurfaceThermoPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", + " reactionLibraries = [\n", + " ('Surface/Methane/Deutschmann_Pt', False),\n", + " ('Surface/Methane/Vlachos_Pt111', False),\n", + " 'BurkeH2O2inArHe'],\n", + " seedMechanisms = [],\n", + " kineticsDepositories = ['training'],\n", + " kineticsFamilies =['surface','default'],\n", + " kineticsEstimator = 'rate rules',\n", + ")\n", + "\n", + "catalystProperties( # default values for Pt(111)\n", + " metal = 'Pt111',\n", + " thermoCoverageDependence = False,\n", + ")\n", + "\n", + "# List of species\n", + "species(\n", + " label='X',\n", + " reactive=True,\n", + " structure=adjacencyList(\"1 X u0\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH4',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH4]\"),\n", + ")\n", + "species(\n", + " label='O2',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u1 p2 c0 {2,S}\n", + " 2 O u1 p2 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='Ar',\n", + " reactive=False,\n", + " structure=SMILES(\"[Ar]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CO2',\n", + " reactive=True,\n", + " structure=SMILES(\"O=C=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='H2O',\n", + " reactive=True,\n", + " structure=SMILES(\"O\"),\n", + ")\n", + "\n", + "species(\n", + " label='H2',\n", + " reactive=True,\n", + " structure=SMILES(\"[H][H]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CO',\n", + " reactive=True,\n", + " structure=SMILES(\"[C-]#[O+]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H6',\n", + " reactive=True,\n", + " structure=SMILES(\"CC\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH2O',\n", + " reactive=True,\n", + " structure=SMILES(\"C=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH3]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C3H8',\n", + " reactive=True,\n", + " structure=SMILES(\"CCC\"),\n", + ")\n", + "\n", + "species(\n", + " label='H',\n", + " reactive=True,\n", + " structure=SMILES(\"[H]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H5',\n", + " reactive=True,\n", + " structure=SMILES(\"C[CH2]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3OH',\n", + " reactive=True,\n", + " structure=SMILES(\"CO\"),\n", + ")\n", + "\n", + "species(\n", + " label='HCO',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH]=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3CHO',\n", + " reactive=True,\n", + " structure=SMILES(\"CC=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='OH',\n", + " reactive=True,\n", + " structure=SMILES(\"[OH]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H4',\n", + " reactive=True,\n", + " structure=SMILES(\"C=C\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3CH',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH]C\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3OO',\n", + " reactive=True,\n", + " structure=SMILES(\"CO[O]\"),\n", + ")\n", + "\n", + "species(\n", + " label='HX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 H u0 p0 c0 {2,S}\n", + " 2 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CO2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {3,D}\n", + " 2 O u0 p2 c0 {3,D}\n", + " 3 C u0 p0 c0 {1,D} {2,D}\n", + " 4 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='COX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 C u0 p0 c0 {1,D} {3,D}\n", + " 3 X u0 p0 c0 {2,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH4X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 H u0 p0 c0 {1,S}\n", + " 5 H u0 p0 c0 {1,S}\n", + " 6 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='OX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 X u0 p0 c0 {1,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,D}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 X u0 p0 c0 {1,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH3X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 H u0 p0 c0 {1,S}\n", + " 5 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CHX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,T}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0 {1,T}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,Q}\n", + " 2 X u0 p0 c0 {1,Q}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='H2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 H u0 p0 c0 {2,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='OHX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,S} {3,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='H2OX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,S} {3,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CHOX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 C u0 p0 c0 {1,D} {3,S} {4,S}\n", + " 3 H u0 p0 c0 {2,S}\n", + " 4 X u0 p0 c0 {2,S}\n", + " '''),\n", + ")\n", + "\n", + "surfaceReactor(\n", + " temperature=(600,'K'),\n", + " initialPressure=(1.0, 'bar'),\n", + " initialGasMoleFractions={\n", + " \"CH4\": 0.108574,\n", + " \"O2\": 0.02088,\n", + " \"Ar\": 0.78547,\n", + " },\n", + " initialSurfaceCoverages={\n", + " \"X\": 1.0,\n", + " },\n", + " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", + " terminationConversion = { \"CH4\":0.95,},\n", + " terminationTime=(10., 's'),\n", + " # terminationConversion={'O2': 0.99,},\n", + " terminationRateRatio=0.05\n", + ")\n", + "\n", + "surfaceReactor(\n", + " temperature=(2000,'K'),\n", + " initialPressure=(1.0, 'bar'),\n", + " initialGasMoleFractions={\n", + " \"CH4\": 0.041866,\n", + " \"O2\": 0.03488,\n", + " \"Ar\": 0.131246,\n", + " },\n", + " initialSurfaceCoverages={\n", + " \"X\": 1.0,\n", + " },\n", + " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", + " terminationConversion = { \"CH4\":0.95,},\n", + " terminationTime=(10., 's'),\n", + " # terminationConversion={'O2': 0.99,},\n", + " terminationRateRatio=0.05\n", + ")\n", + "\n", + "\n", + "simulator(\n", + " atol=1e-18,\n", + " rtol=1e-12,\n", + ")\n", + "\n", + "model(\n", + " toleranceKeepInEdge=0.0,\n", + " toleranceMoveToCore=1e-1,\n", + "# inturrupt tolerance was 0.1 wout pruning, 1e8 w pruning on\n", + " toleranceInterruptSimulation=1e8,\n", + " maximumEdgeSpecies=500000,\n", + " maxNumSpecies=44,\n", + "# PRUNING: uncomment to prune\n", + "# minCoreSizeForPrune=50,\n", + "# prune before simulation based on thermo\n", + "# toleranceThermoKeepSpeciesInEdge=0.5,\n", + "# prune rxns from edge that dont move into core\n", + "# minSpeciesExistIterationsForPrune=2,\n", + "# FILTERING: set so threshold is slightly larger than max rate constants\n", + "# filterReactions=True,\n", + "# filterThreshold=5e8, # default value\n", + ")\n", + "\n", + "options(\n", + " units='si',\n", + " saveRestartPeriod=None,\n", + " generateOutputHTML=True,\n", + " generatePlots=False,\n", + " saveEdgeSpecies=True,\n", + " saveSimulationProfiles=True,\n", + ")\n", + "\n", + "generatedSpeciesConstraints(\n", + " allowed=['input species','reaction libraries'],\n", + " maximumSurfaceSites=1,\n", + ")\n", + "\"\"\"\n", + "\n", + "rmgpy_path = '../rmg.py' # Change to your rmg.py path\n", + "\n", + "\n", + "input_path = os.path.join(base_rmg_run_dir, 'input.py')\n", + "with open(input_path,'w') as f:\n", + " f.write(input_file)\n", + "print(f\"Running RMG with input file at {input_path}...\")\n", + "subprocess.check_call(['python', rmgpy_path, input_path])\n", + "\n", + "\n", + "# repeat for coverage dependent run\n", + "input_path = os.path.join(cov_dep_rmg_run_dir, 'input.py')\n", + "input_file = input_file.replace('thermoCoverageDependence = False', 'thermoCoverageDependence = True')\n", + "with open(input_path,'w') as f:\n", + " f.write(input_file)\n", + "print(f\"Running RMG with input file at {input_path}...\")\n", + "subprocess.check_call(['python', rmgpy_path, input_path])" + ] + }, + { + "cell_type": "markdown", + "id": "4493ff2a", + "metadata": {}, + "source": [ + "# Simulate the RMG mechanisms" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ad06f6c3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Base mechanism contains 23 gas species and 17 surface species\n", + "Coverage-dependent mechanism contains 23 gas species and 17 surface species\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_180850/4041569244.py:4: UserWarning: StickingRate::validate: \n", + "Sticking coefficient is greater than 1 for reaction 'H(12) + X(1) <=> HX(21)'\n", + "at T = 200.0\n", + "at T = 500.0\n", + "at T = 1000.0\n", + "at T = 2000.0\n", + "at T = 5000.0\n", + "at T = 10000.0\n", + "\n", + " surf_base = ct.Interface(base_mech_yaml, 'SURF0', [gas_base])\n", + "/tmp/ipykernel_180850/4041569244.py:4: UserWarning: StickingRate::validate: \n", + "Sticking coefficient is greater than 1 for reaction 'OH(17) + X(1) <=> OHX(31)'\n", + "at T = 200.0\n", + "at T = 500.0\n", + "at T = 1000.0\n", + "at T = 2000.0\n", + "at T = 5000.0\n", + "at T = 10000.0\n", + "\n", + " surf_base = ct.Interface(base_mech_yaml, 'SURF0', [gas_base])\n", + "/tmp/ipykernel_180850/4041569244.py:10: UserWarning: StickingRate::validate: \n", + "Sticking coefficient is greater than 1 for reaction 'H(12) + X(1) <=> HX(21)'\n", + "at T = 200.0\n", + "at T = 500.0\n", + "at T = 1000.0\n", + "at T = 2000.0\n", + "at T = 5000.0\n", + "at T = 10000.0\n", + "\n", + " surf_cov_dep = ct.Interface(cov_dep_mech_yaml, 'SURF0', [gas_cov_dep])\n", + "/tmp/ipykernel_180850/4041569244.py:10: UserWarning: StickingRate::validate: \n", + "Sticking coefficient is greater than 1 for reaction 'OH(17) + X(1) <=> OHX(31)'\n", + "at T = 200.0\n", + "at T = 500.0\n", + "at T = 1000.0\n", + "at T = 2000.0\n", + "at T = 5000.0\n", + "at T = 10000.0\n", + "\n", + " surf_cov_dep = ct.Interface(cov_dep_mech_yaml, 'SURF0', [gas_cov_dep])\n" + ] + } + ], + "source": [ + "# Load mechanism\n", + "base_mech_yaml = os.path.join(base_rmg_run_dir, 'cantera', 'chem_annotated.yaml')\n", + "gas_base = ct.Solution(base_mech_yaml)\n", + "surf_base = ct.Interface(base_mech_yaml, 'SURF0', [gas_base])\n", + "print(f'Base mechanism contains {gas_base.n_species} gas species and {surf_base.n_species} surface species')\n", + "\n", + "\n", + "cov_dep_mech_yaml = os.path.join(cov_dep_rmg_run_dir, 'cantera', 'chem_annotated.yaml')\n", + "gas_cov_dep = ct.Solution(cov_dep_mech_yaml)\n", + "surf_cov_dep = ct.Interface(cov_dep_mech_yaml, 'SURF0', [gas_cov_dep])\n", + "print(f'Coverage-dependent mechanism contains {gas_cov_dep.n_species} gas species and {surf_cov_dep.n_species} surface species')\n", + "\n", + "def get_i_thing(ref_composition, phase):\n", + "\tfor i in range(phase.n_species):\n", + "\t\tif phase.species()[i].composition == ref_composition:\n", + "\t\t\treturn i\n", + "\treturn -1\n", + "\n", + "\n", + "# simulate\n", + "# Set initial conditions\n", + "T = 1000.0 # K\n", + "P = 100000.0 # Pa\n", + "i_CO_base = get_i_thing({'C': 1, 'O': 1}, gas_base)\n", + "i_H2_base = get_i_thing({'H': 2}, gas_base)\n", + "\n", + "initial_mole_fractions = f'{gas_base.species_names[i_CO_base]}: 0.5, {gas_base.species_names[i_H2_base]}: 0.5'\n", + "initial_surface_coverages = 'X(1): 1.0'\n", + "\n", + "\n", + "gas_base.TPX = T, P, initial_mole_fractions\n", + "gas_reactor = ct.IdealGasReactor(gas_base, energy='off') # energy off makes it isothermal\n", + "\n", + "surf_base.TP = T, P \n", + "surf_base.coverages = initial_surface_coverages\n", + "surf_reactor = ct.ReactorSurface(surf_base, gas_reactor)\n", + "\n", + "net = ct.ReactorNet([gas_reactor])\n", + "\n", + "times_base = [net.time]\n", + "gas_concentrations_base = [gas_base.X]\n", + "surf_concentrations_base = [surf_base.coverages]\n", + "\n", + "termination_time = 1.0 # s\n", + "while net.time < termination_time:\n", + " net.step()\n", + " times_base.append(net.time)\n", + " gas_concentrations_base.append(gas_base.X)\n", + " surf_concentrations_base.append(surf_base.coverages)\n", + "\n", + "times_base = np.array(times_base)\n", + "gas_concentrations_base = np.array(gas_concentrations_base)\n", + "surf_concentrations_base = np.array(surf_concentrations_base)\n", + "\n", + "\n", + "# --------------------- Run coverage-dependent simulation ---------------------\n", + "i_CO_cov_dep = get_i_thing({'C': 1, 'O': 1}, gas_cov_dep)\n", + "i_H2_cov_dep = get_i_thing({'H': 2}, gas_cov_dep)\n", + "\n", + "initial_mole_fractions = f'{gas_cov_dep.species_names[i_CO_cov_dep]}: 0.5, {gas_cov_dep.species_names[i_H2_cov_dep]}: 0.5'\n", + "initial_surface_coverages = 'X(1): 1.0'\n", + "\n", + "\n", + "gas_cov_dep.TPX = T, P, initial_mole_fractions\n", + "gas_reactor = ct.IdealGasReactor(gas_cov_dep, energy='off') # energy off makes it isothermal\n", + "\n", + "surf_cov_dep.TP = T, P \n", + "surf_cov_dep.coverages = initial_surface_coverages\n", + "surf_reactor = ct.ReactorSurface(surf_cov_dep, gas_reactor)\n", + "\n", + "net = ct.ReactorNet([gas_reactor])\n", + "\n", + "times_cov_dep = [net.time]\n", + "gas_concentrations_cov_dep = [gas_cov_dep.X]\n", + "surf_concentrations_cov_dep = [surf_cov_dep.coverages]\n", + "\n", + "termination_time = 1.0 # s\n", + "while net.time < termination_time:\n", + " net.step()\n", + " times_cov_dep.append(net.time)\n", + " gas_concentrations_cov_dep.append(gas_cov_dep.X)\n", + " surf_concentrations_cov_dep.append(surf_cov_dep.coverages)\n", + "\n", + "times_cov_dep = np.array(times_cov_dep)\n", + "gas_concentrations_cov_dep = np.array(gas_concentrations_cov_dep)\n", + "surf_concentrations_cov_dep = np.array(surf_concentrations_cov_dep)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "868aa235", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "\n", + "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", + "\n", + "# Plot COX, X, OX, HX\n", + "plt.plot(times_base, surf_concentrations_base[:, get_i_thing({'X': 1}, surf_base)], label='X', color=colors[0])\n", + "plt.plot(times_base, surf_concentrations_base[:, get_i_thing({'O': 1, 'X': 1}, surf_base)], label='OX', color=colors[1])\n", + "plt.plot(times_base, surf_concentrations_base[:, get_i_thing({'H': 1, 'X': 1}, surf_base)], label='HX', color=colors[2])\n", + "plt.plot(times_base, surf_concentrations_base[:, get_i_thing({'C': 1, 'O': 1, 'X': 1}, surf_base)], label='COX', color=colors[3])\n", + "\n", + "# plot cov-dep\n", + "plt.plot(times_cov_dep, surf_concentrations_cov_dep[:, get_i_thing({'X': 1}, surf_cov_dep)], label='X-cov-dep', color=colors[0], linestyle='dashed')\n", + "plt.plot(times_cov_dep, surf_concentrations_cov_dep[:, get_i_thing({'O': 1, 'X': 1}, surf_cov_dep)], label='OX-cov-dep', color=colors[1], linestyle='dashed')\n", + "plt.plot(times_cov_dep, surf_concentrations_cov_dep[:, get_i_thing({'H': 1, 'X': 1}, surf_cov_dep)], label='HX-cov-dep', color=colors[2], linestyle='dashed')\n", + "plt.plot(times_cov_dep, surf_concentrations_cov_dep[:, get_i_thing({'C': 1, 'O': 1, 'X': 1}, surf_cov_dep)], label='COX-cov-dep', color=colors[3], linestyle='dashed')\n", + "\n", + "\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Surface Coverage')\n", + "plt.title('Surface Coverages vs Time')\n", + "plt.legend()\n", + "plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.savefig('cross_surface_coverages.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "6dfaabf1", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the enthalpy of XCO versus coverage\n", + "\n", + "i_XCO_base = get_i_thing({'C': 1, 'O': 1, 'X': 1}, surf_base)\n", + "i_XCO_cov_dep = get_i_thing({'C': 1, 'O': 1, 'X': 1}, surf_cov_dep)\n", + "\n", + "\n", + "coverages = np.linspace(0, 1, 10)\n", + "enthalpies_base = []\n", + "enthalpies_cov_dep = []\n", + "\n", + "entropies_base = []\n", + "entropies_cov_dep = []\n", + "\n", + "T = 300.0 # K\n", + "P = ct.one_atm # Pa\n", + "for coverage in coverages:\n", + "\n", + " surf_base.TPX = T, P, f'X(1): {1.0 - coverage}, {surf_base.species_names[i_XCO_base]}: {coverage}'\n", + " surf_cov_dep.TPX = T, P, f'X(1): {1.0 - coverage}, {surf_cov_dep.species_names[i_XCO_cov_dep]}: {coverage}'\n", + "\n", + " enthalpies_base.append(surf_base.standard_enthalpies_RT[i_XCO_base])\n", + " enthalpies_cov_dep.append(surf_cov_dep.standard_enthalpies_RT[i_XCO_cov_dep])\n", + "\n", + " entropies_base.append(surf_base.standard_entropies_R[i_XCO_base])\n", + " entropies_cov_dep.append(surf_cov_dep.standard_entropies_R[i_XCO_cov_dep])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f28971e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(coverages, enthalpies_base, label='Base', color=colors[0])\n", + "plt.plot(coverages, enthalpies_cov_dep, label='Coverage Dependent', color=colors[1])\n", + "\n", + "\n", + "# these values copied from Cantera example: https://cantera.org/dev/examples/python/thermo/coverage_dependent_surf.html\n", + "# provide discrete enthalpy and entropy values calculated with DFT\n", + "# array of CO* coverage\n", + "dft_covs = [0., 0.11, 0.22, 0.33, 0.44, 0.56, 0.67, 0.78, 0.89, 1.0]\n", + "# array of nondimensionalized DFT-derived CO* enthalpy values\n", + "dft_hrts = [-112.8 , -112.8 , -108.78, -105.76, -106.11, -102.51, -96.83,\n", + " -85.03, -78.76, -72.19]\n", + "\n", + "plt.scatter(dft_covs, dft_hrts, label='DFT', color=colors[2], marker='x')\n", + "\n", + "\n", + "plt.xlabel('Coverage of XCO')\n", + "plt.ylabel('Standard Enthalpy / RT')\n", + "plt.title('Standard Enthalpy of XCO vs Coverage')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "8ef85c64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dGgqFAjExMVrbni1bu3YtkpKSsHr1arz33nto3rw5GjZsCHNz8xyPndNf3WXKlFH/gszrXEVJbtcRHR2tVX779m0AgJOTk0Z5bvfX3Nwctra26rKPPvoIaWlpWLBgAebNm4f09HT4+/s/N0Zd48kvEcGgQYNgZWWFOXPmYMKECfDx8cEHH3yAhIQEAECHDh0AqL4/8qNdu3a4c+cODh06lOP2x48fY9u2bahdu/Zzk9533nkHFhYWWLhwITIyMrB48WL06NEDpUuXVtcxMzPDmDFjcOLECcTGxuLvv//GjRs30KFDB3VrSk46dOiAjIwMbNiw4bnXlJVE5/Y1MDEx0Yhp0KBBOHToEMLDwxESEoLo6GgMGjRIvT3r6xUQEJBjC+XRo0dRr149jfPk9H26ZMkS9O/fH9999x06dOiAxo0bo2HDhlqJUX5/LrPi+u2333KNK6ckiQyLyQ3pVU6/+ACom4ez/soGcm9dUCgUMDc31/hFFhMTg3Xr1r1QTDY2NmjcuDFWr16t0fKTkJCg9Us965zZWxFEBPPmzcv3+Vq3bo2EhASsX79eo3zZsmUvEn6ByLoeXVpz2rRpg7CwMJw4cUKjfNGiRVAoFFrv4Mnt/rZo0QKmpqbqcjc3N/Tu3RuzZs3CnDlz0K1bN43HannFs3PnTnUykz0ea2trNGnSJN/Xlt2vv/6KPXv2YPbs2XB2doZSqcTChQtx+/ZtjBs3DgDQoEEDdOrUCfPnz9d6HJfl2LFjiIqKAgCMHj0aVlZWGDFiRI6tImPHjsXDhw/xxRdfPDe+0qVLo0ePHli0aBE2btyImJgYjUdSzypVqhTefPNNDBs2DLGxsXm+uHHw4MFwdXXFp59+ilu3buVYZ/Xq1QCAatWqoXz58li2bJn6kRqgeuwbHBwMPz8/WFtbq8vfeecdWFpaYuHChVi4cCHKly+P9u3bq7dXq1YNVapUwenTp3NsoWzYsKHG4+bcKBQKrVa/f//9V+t6WrZsiXPnziEsLEyjfPny5RrrzZo1Q6lSpRAWFpZrXLn9sUMGZMgOP2T86tSpI506dZJZs2bJzp07Zfv27fLTTz+Jm5ub2NraagzPHDBggFhYWMjy5cvlyJEj6m0LFixQj8jZsWOHLFy4UCpVqiRVqlTR6vyLXDqOenp6yoABA9TrW7duFRMTE2nevLmsWbNGVq1aJY0aNRIPDw+NY4aHh4u5ubm0atVKNm3aJKtXr5Z27dqpz529Q27WEOpnJSUlSdWqVcXBwUF+//132bJli4waNUrn0VLfffederRP9uXZDsVdunTROkbLli01OmtfvXpVAEiPHj1k7969cvToUbl//36e15E1WsrV1VXmzp2rHvWlUChk6NCh6nrPjpZavXq1+v6amZnJvn37tI59+PBhdQfp/I4+yRotVbVqVVmyZIls2rRJ+vbtKwDkxx9/1Kib2/fFsyIiIsTKykrefvttrW1ZrwnYtm2biIjcu3dPPTLI399f1q1bJ3v27JEVK1bIe++9J6ampnLq1Cn1/qtWrRILCwupUaOGzJs3T/bs2SP//POPdOrUSQDI2LFj83XdIiJbtmwRAOLu7i7u7u5a74zq2rWrfPbZZ+oRQ4sWLRIvLy/x9PTMdcRilsOHD0vZsmWlbNmyMmXKFNm6davs3r1b5s2bJy1btpRSpUqp62aNlurcubOsW7dOVq5cKY0aNdIYLZXdO++8I87OzmJubi6ff/651vadO3eKhYWFtG/fXpYtW6Ye7fTdd9/Jm2++qa6X1aH46NGjWsfo37+/WFhYyC+//CI7duyQH3/8UcqWLSvu7u4aPwO3bt3SGC21efNm6devn/q1FFmjC0VUo6VMTEykT58+8s8//0hoaKisWrVKJk6cKP7+/nneTzIMJjekVytWrJB3331XqlSpIra2tqJUKqVChQrSr18/CQsL06h77do1ad++vdjZ2Wm850ZE5PvvvxcvLy+ND4ec3kmT3+RGRGT9+vVSt25dMTc3lwoVKsj333+f4zE3bNggPj4+YmlpKeXLl5dx48bJ5s2b853ciIjcvHlTevXqJba2tmJnZye9evWSAwcOFMhoqQkTJmhcZ36SGxGR6dOni7e3t5iamub4npucXL9+Xd59910pU6aMKJVKqVatmkydOjXX99xMmTJF3N3dxdzcXOrXry9btmzJ9Tq9vLykRo0aed6LZ509e1a6desmDg4OYm5uLj4+Pjnez/wkNxkZGeLn5yeurq7y4MEDre2pqani4+Mjnp6e6tF4ycnJMmPGDPHz8xN7e3sxMzOTcuXKyRtvvCH//vuv1jHOnz8vAwYMEHd3d1EqleLo6CgdO3bMse7zYs1KxLN//bNMmzZNmjZtKk5OTurv78GDB8u1a9fydfyYmBgZP3681KpVS6ytrcXCwkIqV64sH374oZw9e1aj7tq1a+WVV14RS0tLsbGxkTZt2sj+/ftzPO7WrVvV37cXL17Msc7p06flrbfeEmdnZ1EqleLq6iqvvfaazJkzR10nr+Tm4cOHMnjwYHF2dhZra2tp3ry57N27N8efgXPnzknbtm3F0tJSHB0dZfDgwep3LGV/946ISGhoqHTp0kUcHR1FqVRK+fLlpUuXLkX2pYYlnUIkW3siEdFLunbtGry9vTF16lSMHTs2X/ucOXMGPj4+mDlzprrzKJEh/O9//8Pff/+NBw8e8HFTMca5pYjIYK5cuYLr16/j888/h5ubG+cVo0L11VdfoVy5cqhYsSISExOxceNG/Pnnn/jiiy+Y2BRzTG6IyGC+/vprLF68GDVq1MA///yj0QGVSN+USiWmTp2KmzdvIj09HVWqVMHPP/+MUaNGGTo0ekl8LEVERERGhUPBiYiIyKgwuSEiIiKjwuSGiIiIjEqJ61CcmZmJ27dvw87OLs8J6oiIiKjoEBEkJCSgXLlyGpO15qTEJTe3b99+7sR0REREVDTduHED7u7uedYpcclN1twkN27cgL29vYGjISIiovyIj4+Hh4dHvuYYK3HJTdajKHt7eyY3RERExUx+upSwQzEREREZFSY3REREZFSY3BAREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNERERGRUmN2Q0ElITEJMUk+O2mKQYJKQmFHJERERkCExuyCgkpCbAf7s/BoUM0kpwYpJiMChkEPy3+zPBISIqAZjckFFISktCbHIsbibe1EhwshKbm4k3EZsci6S0JANHSkRE+sbkhoyCq40rgjoGwd3WXZ3gnLp7Sp3YuNu6I6hjEFxtXA0dKhER6ZlCRMTQQRSm+Ph4ODg4IC4ujrOCG6HsLTVZmNgQERV/unx+s+WGjIqrjSsCWwRqlAW2CGRiQ0RUgjC5IaMSkxSDgL0BGmUBewNyHUVFRETGh8kNGY3sj6Tcbd2xuNNijT44THCIiEoG9rkpICKC5LSMAjse6eZO0h34b/PHraRbKG9THnPazYGLjUuu5UREpF9WSlMoFIoCO54un99MbgrI49R01PxyS4Edj4iIqDgL+6oDrM3NCux47FBMREREJVbBpVQlnJXSFGFfdTB0GEREREWCldLUYOdmclNAFApFgTa/ERER0YvhYykiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjYtDkZvbs2ahbty7s7e1hb28PPz8/bN68Odf6u3fvhkKh0FouXLhQiFETERFRUWZmyJO7u7vj+++/R+XKlQEAf/31F15//XWcPHkStWrVynW/iIgI2Nvbq9fLli2r91iJiIioeDBoctOtWzeN9W+//RazZ8/GoUOH8kxunJ2dUapUKT1HR0RERMVRkelzk5GRgeXLlyMpKQl+fn551q1fvz7c3NzQpk0b7Nq1K8+6KSkpiI+P11iIiIjIeBk8uTl79ixsbW1hYWEBf39/rFmzBjVr1syxrpubG+bOnYvg4GCsXr0a1apVQ5s2bbBnz55cjx8YGAgHBwf14uHhoa9LISIioiJAISJiyABSU1MRFRWFR48eITg4GH/++SdCQ0NzTXCe1a1bNygUCqxfvz7H7SkpKUhJSVGvx8fHw8PDA3FxcRr9doiIiKjoio+Ph4ODQ74+vw3a5wYAzM3N1R2KGzZsiKNHj+LXX3/FH3/8ka/9mzRpgiVLluS63cLCAhYWFgUSKxERERV9Bn8s9SwR0WhpeZ6TJ0/Czc1NjxERERFRcWLQlpvPP/8cnTp1goeHBxISErB8+XLs3r0bISEhAICAgADcunULixYtAgBMnz4dXl5eqFWrFlJTU7FkyRIEBwcjODjYkJdBRERERYhBk5s7d+6gX79+iI6OhoODA+rWrYuQkBC0a9cOABAdHY2oqCh1/dTUVIwdOxa3bt2ClZUVatWqhX///RedO3c21CUQERFREWPwDsWFTZcOSURERFQ06PL5XeT63BARERG9DCY3REREZFSY3BAREZFRYXJDRERERoXJDRERERkVJjdERERkVJjcEBERkVFhckNERERGhckNURGTkJqAmKSYHLfFJMUgITWhkCMiIipemNwQFSEJqQnw3+6PQSGDtBKcmKQYDAoZBP/t/kxwiIjywOSGqAhJSktCbHIsbibe1EhwshKbm4k3EZsci6S0JANHSkRUdDG5ISpCXG1cEdQxCO627uoE59TdU+rExt3WHUEdg+Bq42roUImIiixOnElUBGVvqcnCxIaISjJOnElUzLnauCKwRaBGWWCLQCY2RET5wOSGqAiKSYpBwN4AjbKAvQG5jqIiIqKnmNwQFTHZH0m527pjcafFGn1wmOAQEeWNyQ1REfJsYhPUMQj1nOtpdTJmgkNElDsmN0RFiI3SBo5Wjlqdh7OPonK0coSN0sbAkRIRFV0cLUVUxCSkJiApLSnHzsMxSTGwUdrAztzOAJERERmOLp/fZoUUExHlk525Xa7JC0dLERE9Hx9LERERkVFhckNEBY6TfxKRITG5IaICxck/icjQmNwQUYHi5J9EZGhMboioQHHyTyIyNA4FJyK94OSfRFSQOHEmERkcJ/8kIkNhckNEesHJP4nIUJjcEFGB4+SfRGRITG6IqEBx8k8iMjQmN0RUoDj5JxEZGkdLEVGB4+SfRFTQOHEmERkUJ/8kIkPiYykiIiIyKkxuiIiIyKgYNLmZPXs26tatC3t7e9jb28PPzw+bN2/Oc5/Q0FD4+vrC0tISFStWxJw5cwopWiIiIioODJrcuLu74/vvv8exY8dw7NgxvPbaa3j99ddx/vz5HOtHRkaic+fOaNGiBU6ePInPP/8cI0eORHBwcCFHTkREREVVkRst5ejoiKlTp2Lw4MFa28aPH4/169cjPDxcXebv74/Tp0/j4MGD+To+R0sREREVP8VybqmMjAwsX74cSUlJ8PPzy7HOwYMH0b59e42yDh064NixY0hLS8txn5SUFMTHx2ssREREZLwMntycPXsWtra2sLCwgL+/P9asWYOaNWvmWDcmJgYuLi4aZS4uLkhPT8f9+/dz3CcwMBAODg7qxcPDo8CvgYiIiIoOgyc31apVw6lTp3Do0CF89NFHGDBgAMLCwnKtr1AoNNaznqo9W54lICAAcXFx6uXGjRsFFzwREREVOQZ/iZ+5uTkqV64MAGjYsCGOHj2KX3/9FX/88YdWXVdXV8TEaM5Hc/fuXZiZmaFMmTI5Ht/CwgIWFhYFHzgREREVSQZvuXmWiCAlJSXHbX5+fti2bZtG2datW9GwYUMolcrCCI+IiIiKOIMmN59//jn27t2La9eu4ezZs5gwYQJ2796Nvn37AlA9Uurfv7+6vr+/P65fv44xY8YgPDwcCxYswPz58zF27FhDXQIREREVMQZ9LHXnzh3069cP0dHRcHBwQN26dRESEoJ27doBAKKjoxEVFaWu7+3tjU2bNmH06NGYOXMmypUrhxkzZqBXr16GugQiIiIqYorce270je+5ISIiKn6K5XtuiIiIiAoCkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjEqBJTdPnjzBTz/9VFCHIyIiInohOiU39+/fx7///outW7ciIyMDAJCWloZff/0VXl5e+P777/USJBEREVF+meW34oEDB9ClSxfExcVBoVCgYcOGCAoKQo8ePZCZmYkvvvgC77//vj5jJSIiInqufLfcTJw4ER06dMCZM2cwatQoHD16FF27dsUXX3yBS5cuYfjw4bC2ttZnrERERETPpRARyU9FJycnhIaGolatWnj8+DHs7OywfPly9O7dW98xFqj4+Hg4ODggLi4O9vb2hg6HiIiI8kGXz+98t9zExsaibNmyAABra2tYW1ujfv36LxcpERERUQHLd58bhUKBhIQEWFpaQkSgUCjw+PFjxMfHa9RjawgREREZUr6TGxFB1apVNdazt9xkJTxZo6iIiIiIDCHfyc2uXbv0GQcRERFRgch3ctOyZUt9xkFERERUIDj9AhERERkVJjdERERkVJjcEBERkVFhckNERERGJd/JTbly5fDRRx9h8+bNSE1N1WdMRERERC8s38nNsmXLYG1tjZEjR8LJyQm9e/fG4sWLERsbq8/4iIiIiHSS77mlsjt//jzWr1+PdevW4eTJk/Dz88Prr7+O7t27o1KlSvqIs8BwbikiIqLiRy9zS2VXq1YtBAQE4NChQ4iKikLfvn2xc+dO1KlTB7Vr18a///77QoETERERvayX7lDs4uKCIUOGYMOGDbh//z6+/vprWFhY5GvfwMBANGrUCHZ2dnB2dkaPHj0QERGR5z67d++GQqHQWi5cuPCyl0JERERGIN9vKM4Pa2tr9OzZM9/1Q0NDMWzYMDRq1Ajp6emYMGEC2rdvj7CwMNjY2OS5b0REhEazVNaM5URERFSyFWhyo6uQkBCN9aCgIDg7O+P48eN49dVX89zX2dkZpUqV0mN0REREVBwVqffcxMXFAQAcHR2fW7d+/fpwc3NDmzZtDDqpZ0JqAmKSYnLcFpMUg4TUhEKO6MUYy3UQEREVmeRGRDBmzBg0b94ctWvXzrWem5sb5s6di+DgYKxevRrVqlVDmzZtsGfPnhzrp6SkID4+XmMpKAmpCfDf7o9BIYO0EoOYpBgMChkE/+3+RT4xMJbrICIiAl4guVm4cCEeP35c4IEMHz4cZ86cwd9//51nvWrVqmHIkCFo0KAB/Pz8MGvWLHTp0gU//fRTjvUDAwPh4OCgXjw8PAos5qS0JMQmx+Jm4k2NxCArIbiZeBOxybFISksqsHPqg7FcBxEREfACyU1AQABcXV0xePBgHDhwoECCGDFiBNavX49du3bB3d1d5/2bNGmCS5cu5bgtICAAcXFx6uXGjRsvG66aq40rgjoGwd3WXZ0YnLp7Sp0QuNu6I6hjEFxtXAvsnPpgLNdBREQEvEByc/PmTSxZsgQPHz5E69atUb16dfzwww+Iicm5v0ZeRATDhw/H6tWrsXPnTnh7e+t8DAA4efIk3NzcctxmYWEBe3t7jaUgPZsY9Nvcr1gmBMZyHURERDonN6ampujevTtWr16NGzdu4H//+x+WLl2KChUqoHv37li3bh0yMzPzdaxhw4ZhyZIlWLZsGezs7BATE4OYmBgkJyer6wQEBKB///7q9enTp2Pt2rW4dOkSzp8/j4CAAAQHB2P48OG6XkqBcbVxRWCLQI2ywBaBxS4hMJbrINIXdrwnKh5eqkOxs7MzmjVrBj8/P5iYmODs2bMYOHAgKlWqhN27dz93/9mzZyMuLg6tWrWCm5ubelmxYoW6TnR0NKKiotTrqampGDt2LOrWrYsWLVpg3759+Pfff/HGG2+8zKW8lJikGATsDdAoC9gbkOsvwaLKWK6DSB/Y8Z6o+Hih5ObOnTv46aefUKtWLbRq1Qrx8fHYuHEjIiMjcfv2bbzxxhsYMGDAc48jIjkuAwcOVNdZuHChRqL06aef4vLly0hOTkZsbCz27t2Lzp07v8hlFIjsnW7dbd2xuNNijb4rxSUxMJbrINIXdrwnKj50njizW7du2LJlC6pWrYoPPvgA/fv313ovze3bt+Hu7p7vx1OFqSAnznw2Icjqm5JbeVFlLNdBpG/P/kwEtghEwN4A/owQFQK9Tpzp7OyM0NBQnDt3Dh9//HGOL9xzc3NDZGSkrocudmyUNnC0ctT6pZa9c66jlSNslHlPJWFoxnIdRPrGjvdExYPOLTfFXUG23ACq5/BJaUk5/lKLSYqBjdIGduZ2L30efTOW6yAqDKfunkK/zf3U64s7LUY953qGC4ioBNBryw0A7NixA127dkWlSpVQuXJldO3aFdu3b3+hYIs7O3O7XP9ac7VxLTYJgbFcB5G+seM9UdGnc3Lz+++/o2PHjrCzs8OoUaMwcuRI2Nvbo3Pnzvj999/1ESMRUZHAjvdExYPOj6XKly+PgIAArffKzJw5E99++y1u375doAEWtIJ+LEVEJQM73hMZll4fS8XHx6Njx45a5e3bty/QSSmJiIoSdrwnKj7MdN2he/fuWLNmDcaNG6dRvm7dOnTr1q3AAiMiKkrszO0wp+2cHDveZyU47HhPVDTonNzUqFED3377LXbv3g0/Pz8AwKFDh7B//3588sknmDFjhrruyJEjCy5SIiIDszO3yzV54aMooqJD5z43+Z3cUqFQ4OrVqy8UlD6xzw0REVHxo8vnt84tNyXh5XxERERUfL3UxJlZc0ERERERFRUvlNwsWrQIderUgZWVFaysrFC3bl0sXry4oGMjIiIi0pnOj6V+/vlnTJw4EcOHD0ezZs0gIti/fz/8/f1x//59jB49Wh9xEhEREeXLC3UonjJlCvr3769R/tdff2Hy5MlFvk8OOxQTEREVP3p9iV90dDSaNm2qVd60aVNER0frejgiIiKiAqVzclO5cmWsXLlSq3zFihWoUqVKgQRFRERE9KJ07nMzZcoU9OnTB3v27EGzZs2gUCiwb98+7NixI8ekh4iIipaE1IQc37QMqObQ4puWqbjTueWmV69eOHLkCJycnLB27VqsXr0aTk5OOHLkCHr27KmPGImIqIAkpCbAf7t/jrOYZ00C6r/dHwmpCQaKkOjl6dRyk5aWhv/973+YOHEilixZoq+YiIhIT5LSkhCbHIubiTcxKGRQjrObZ9Vj6w0VVzq13CiVSqxZs0ZfsRARkZ5ln8U8K8E5dfeUOrF5dtZzouJI58dSPXv2xNq1a/UQChERFYZnE5x+m/sxsSGjonOH4sqVK+Prr7/GgQMH4OvrCxsbG43tnAmciKjoc7VxRWCLQPTb3E9dFtgikIkNGYUCnRW8qM4Enh1f4kdEBK0+NgDYckNFGmcFJyKiXGVPbNxt3RHYIhABewO0OhkTFVc697n56quv8PjxY63y5ORkfPXVVwUSFBER6ceziU1QxyDUc66n1cn42WHiRMWJzsnNlClTkJiYqFX++PFjTJkypUCCIiIi/bBR2sDRylHrEVT2TsaOVo6wUdo850hERZfOj6VEBAqFQqv89OnTcHR0LJCgiIhIP+zM7TCn7Zwc31CcleDwDcVU3OU7uSldujQUCgUUCgWqVq2qkeBkZGQgMTER/v7+egmSiIgKjp25Xa7JC/vakDHId3Izffp0iAjef/99TJkyBQ4ODupt5ubm8PLygp+fn16CJCIiIsqvfCc3AwYMAKAaCt60aVMolUq9BUVERET0onTuc9OyZUtkZmbi4sWLuHv3LjIzMzW2v/rqqwUWHBEREZGudE5uDh06hHfffRfXr1/Hs+//UygUyMjIKLDgiIiIiHSlc3Lj7++Phg0b4t9//4Wbm1uOI6eIiIiIDEXn5ObSpUtYtWoVKleurI94iIiIiF6Kzi/xe+WVV3D58mV9xEJERET00nRObkaMGIFPPvkECxcuxPHjx3HmzBmNRReBgYFo1KgR7Ozs4OzsjB49eiAiIuK5+4WGhsLX1xeWlpaoWLEi5syZo+tlEBERkZHSeVZwExPtfEihUKjfXKxLh+KOHTvi7bffRqNGjZCeno4JEybg7NmzCAsLg41Nzq/+joyMRO3atTFkyBB8+OGH2L9/P4YOHYq///4bvXr1eu45OSs4ERFR8aPL57fOyc3169fz3O7p6anL4TTcu3cPzs7OCA0NzXVI+fjx47F+/XqEh4ery/z9/XH69GkcPHjwuedgckNERFT86PL5rXOH4pdJXp4nLi4OAPKco+rgwYNo3769RlmHDh0wf/58pKWlab1cMCUlBSkpKer1+Pj4AoyYiIiIipp897kZOnSoxmzgixcv1lh/9OgROnfu/MKBiAjGjBmD5s2bo3bt2rnWi4mJgYuLi0aZi4sL0tPTcf/+fa36gYGBcHBwUC8eHh4vHCMREREVfflObv744w88fvxYvT5s2DDcvXtXvZ6SkoItW7a8cCDDhw/HmTNn8Pfffz+37rPv1sl6spbTO3cCAgIQFxenXm7cuPHCMRIREVHRl+/HUs92zdGxq06eRowYgfXr12PPnj1wd3fPs66rqytiYmI0yu7evQszMzOUKVNGq76FhQUsLCwKLFYiIiIq2nQeCl6QRATDhw/H6tWrsXPnTnh7ez93Hz8/P2zbtk2jbOvWrWjYsCEn8yQiIiLDJjfDhg3DkiVLsGzZMtjZ2SEmJgYxMTFITk5W1wkICED//v3V6/7+/rh+/TrGjBmD8PBwLFiwAPPnz8fYsWMNcQlERERUxOg0WurLL7+EtbU1ACA1NRXffvstHBwcAECjP05+zZ49GwDQqlUrjfKgoCAMHDgQABAdHY2oqCj1Nm9vb2zatAmjR4/GzJkzUa5cOcyYMSNf77ghIiIi45fv99y0atUqX5Nk7tq166WD0ie+54aIiKj40ct7bnbv3v2ycRERERHpnUH73BAREREVNCY3REREZFSY3BAREZFRYXJDRERERoXJDRERERmVfI2WOnPmTL4PWLdu3RcOhoiIiOhl5Su5qVevHhQKBUTkue+6ycjIKJDAiIiIiF5Evh5LRUZG4urVq4iMjERwcDC8vb0xa9YsnDx5EidPnsSsWbNQqVIlBAcH6zteIiIiojzlq+XG09NT/f/evXtjxowZ6Ny5s7qsbt268PDwwMSJE9GjR48CD5KIiIgov3SaWwoAzp49m+Ps3d7e3ggLCyuQoIhKrLRkIOmeakn879+ku0BKImBmCZhZAEor1b9mlk8XpWXe62YWQD6mTyEiMgY6Jzc1atTAN998g/nz58PS0hIAkJKSgm+++QY1atQo8ACJijUR4MkjzUQl6f5/ycvdp4lMVjKTmqC/WHRJhvKTLKmTLKvc100tABMOyiSiwqVzcjNnzhx069YNHh4e8PHxAQCcPn0aCoUCGzduLPAAiYqcjLSnCUpWsqKVqGRLYjLTdDu+qTlgU/bpYusMWNgB6SlA+pP/lhRVK096CpCenPs6ss2Lm7VvYTO1eJogWdgBZasDLrUB19qqf0t5MgEiogKV71nBs3v8+DGWLFmCCxcuQERQs2ZNvPvuu7CxsdFHjAWKs4JTjlISc0lO7mo/Ikp+qPvxLeyzJStZiYszYOOkSl6yr1s6FMwjJBFVIqaV/Dx5uqQ90W09v0mV6DBq0twOcKmZLeGpo1o3L/q/T4io8Ojy+a1TcpOWloZq1aph48aNqFmz5ksHaghMbkqQ1CTgUdTzk5Wk+0DaY92OrTABrLMSE6f/EpMcEpeshEZpqZ9rLKoy0nNOqh4/AO6cB+6cA2LOAvcuABmpORxAATh6/5fw1Hma+Dh4sO8QUQmly+e3To+llEolUlJSnvuuG6JClZkJPLr+34fmfx+cd84DsVeh8Vjmecys8mhVyfaIyKYsYOXIRyl5MTUDTO1Uj6GeVbHl0/9npAEPLgMx54A7Z//79zyQGKP6+sVeBcLXP61v4QC41Hr6SMu1NuBcU9W/h4joPzo/lvr+++9x4cIF/PnnnzAz07nLjsGx5aaYexIH3Al7msDcOQ/cDQNSE3Oub+kA2Lo8J1n5r+XF3IatAkVF4r3/vsbn/kt4zgH3InLuv6QwARwrZUt4/mvpsS/HryeREdHbYykA6NmzJ3bs2AFbW1vUqVNHq5/N6tWrdY+4EDG5KSYyM1R/tWclMVl/0cdF5Vzf1PxpR1WXWk8XW+fCjZv0Jz0VuH/x6SOtrMTn8f2c61uV/u/7IVvn5bLVS94jQiIjobfHUgBQqlQp9OrV64WDI9LyODZbS8x//94Nz31kj727ZgLjUhsoUwkwVRZu3FS4zMxVSYprbcDn7aflCXeyPdL67/vnXoSq4/e1vaoli8IUcKqa7dFWHdW/ti5s5SEyIi80Wqo4Y8uNAaWnAg8uaSYxd84DCdE511daA841niYwLrVVo2isShdu3FT8pKeoOitnJTxZLT25jXSzdnraupPV0uNUTZVQEVGRoNfHUsUdk5tCIKIanZQ9gblzXvVhk9s7X0p7PfNIqbaqzMS0MCMnYyaiSqQ1Oi+fU3Volkzt+iZKoGy1p9+PWS09tmULP3Yi0n9ys2rVKqxcuRJRUVFITdUcxnnixAldD1eomNwUsLQnqqTl2ZFKufWDsLDXfqTkXCPnUTVEhSEtWfUYNHvn5TvnVJ3Xc2LrArjWBap3Bmr1ZEsiUSHRa5+bGTNmYMKECRgwYADWrVuHQYMG4cqVKzh69CiGDRv2wkFTEScCxN3UfqT04HLOL2zLGsGSfdiuSy2+p4SKHqUVUL6Basmi/n4/p9nSE3sVSLwDXN6mWjaPB6q0V/UBqtJeNe0EFZqE1AQkpSXB1cZVa1tMUgxslDawM+cfTiWRzi031atXx6RJk/DOO+/Azs4Op0+fRsWKFfHll18iNjYWv//+u75iLRBsucmH1Meq4dXPjlRKyeUv2eyjUrJaZMpWB8ytCzduIn1LTVK18lzfD5xZqfoZyWJZStWS4/M24PEKk3g9S0hNgP92f8QmxyKoY5BGghOTFINBIYPgaOWIOW3nMMExEnp9LGVtbY3w8HB4enrC2dkZ27Ztg4+PDy5duoQmTZrgwYMHLxW8vjG5ycGTOODGEeDaPuD6AeD2yZz7xpiYqTpZZn+k5FILsHPlL3IqmWLOAWdWAGf/0ewYX8oTqNtHtThVNlx8RiwrgbmZeBPutu7qBCe3cir+9PpYytXVFQ8ePICnpyc8PT1x6NAh+Pj4IDIyEiWsb3LxlfQAiDqo+uvz+n7VSJJnO1TaOP/3OCnbSCWnqhw9QpRd1tD0tpNVQ85Pr1C9UfnRdWDPj6qlvK8qyandS/XCSCoQrjauCOoYpE5kBoUMQmCLQATsDShWiQ0fremHzi03H3zwATw8PDBp0iTMmTMHY8aMQbNmzXDs2DG88cYbmD9/vr5iLRAlsuUmIea/ROYAcG0/cC9cu05pb8CzGeDVDPBsqvrLk60xRLpLfQxEbAJOLweu7HzaJ83EDKjcFqj7FlCtM6eMKCDZW2qyFKfEho/W8k+vj6UyMzORmZmpnnph5cqV2LdvHypXrgx/f3+Ymxftv+yNPrkRUU0Wef3A05aZ2Kva9cpWVyUxnv8lM/blCj9WImOXeBc4F6x6dHX75NNyczug5uuATx/AsznnKXtJp+6eQr/N/dTrizstRj3neoYLKJ/4aE03fM9NHowuuRFRjVjK3jITf/OZSgrVfDtZLTMV/Ng8TlTY7kWoOiGfWak5jYh9eaBOb1VHZOcahouvmCrOLTeAdoJTHB+tFZYCT27OnDmT75PXrVs333UNodgnN5mZqpFM6paZA0DSXc06JmZAufr/tcw0BzwaA1alDBIuET0jM1PV5+3MCuD8Ws1RiK51gLpvA3XeVHXUpzwZS2JQ3BO0wlLgyY2JiQkUCgVEBIrn9MPIyMjhnSdFSLFLbjLSgZjT/yUz/y1PHmnWMbUA3BupkhmvZqr/m9vkeDgiKkLSngCXtqg6Il/a+nSUosIEqNhK1RG5elfAwtagYRZFxvZIp7g+WitMBT5aKjIyUv3/kydPYuzYsRg3bhz8/PwAAAcPHsS0adPw448/vkTYBEA1J86tE09bZW4cBlITNesobYAKrzxtmSnfgC8PIyqOlJaqvjc1X1dNIHt+teqx1Y3Dqs7IV3aq5lir3lXVP8e7FWCq8yBXo2SjtIGjlSMAaCQw2UdROVo5wkZZ9P/Qi0mKQcDeAI2ygL0BxSYxK4p07nPTuHFjTJ48GZ07d9Yo37RpEyZOnIjjx48XaIAFrci13KQmATePPm2VuXlUezZsSwegQtOnLTOuPvwFR2TMYq8CZ/4BzizXHBBg6wLUflM14srNp8SPaDSGYdTG8mitMOi1Q7GVlRVOnDiBGjU0O76Fh4ejQYMGSE5O1j3iQmTw5OZJHBB1+GnLzO0TQGa6Zh2bspojmZxrcTQFUUkkAtw6rhpWfi4YSI59uq1sddVjqzq9gVIehouRXpixPVrTN70mNw0aNECNGjUwf/58WFpaAgBSUlLw/vvvIzw8nBNnPivpARB14GkH4JxemGdf/mki49UcKFO5xP9FRkTPSE8FruxQJToRm4GMlKfbvFqoWnNqvq5q6aVige+50Y1ek5sjR46gW7duyMzMhI+PDwDg9OnTUCgU2LhxIxo3bpzvY+3ZswdTp07F8ePHER0djTVr1qBHjx651t+9ezdat26tVR4eHo7q1avn65x6T27io5+2ylw/kPML8xwrarbM8IV5RKSLJ3FA2DpV/5xre5+Wm1oA1TqphpVXasM3ihcDxvBorbDodfqFxo0bIzIyEkuWLMGFCxcgIujTpw/effdd2Njo1nErKSkJPj4+GDRoEHr16pXv/SIiIjQurGzZsjqdVy/uXgCWv5PLC/Nq/JfMNOUL84jo5Vk6AA36q5ZHN4CzK1Ujru5HAGFrVYuVo2rKB5+3VVNA8A+oIsnO3C7X5IWPol5ckXmJn0KhyHfLzcOHD1GqVKkXOo/eWm5SEoDvPQHI0xfmeTblC/OIqHCIANGnVa05Z//RfP+VY6X/JvLsrWo5JiqG9NpyAwAXL17E7t27cffuXWRmavYf+fLLL1/kkDqpX78+njx5gpo1a+KLL77I8VFVlpSUFKSkPH02HR8fr5+gLOyAgRtVE03ymTcRFTaFAihXT7W0+wqI3K1qzbmwEYi9Auz+TrV4vKLqn1PrDcDa0cBBE+mHzi038+bNw0cffQQnJye4urpqvNRPoVC8cIfi/LTcREREYM+ePfD19UVKSgoWL16MOXPmYPfu3Xj11Vdz3Gfy5MmYMmWKVnmRGQpORKRPKYmqBOf0ciAy9OmABhMlUKU90Pxj1VvMiYo4vXYo9vT0xNChQzF+/PiXClIrkHwkNznp1q0bFAoF1q9fn+P2nFpuPDw8mNwQUckTHw2cW6Wa+iHm7NPy2m8CbSdzSDkVabokNzq/POXhw4fo3bv3CwdX0Jo0aYJLly7lut3CwgL29vYaCxFRiWTvBjQdAfjvAz46CNR/D4BClfD83hDY+Y2qpYeomNM5uenduze2bt2qj1heyMmTJ+Hm5mboMIiIiheXmsDrM4EPQ1XTuKQ/AfZMBX7zBU4uVU3wSVRM6dyhuHLlypg4cSIOHTqEOnXqQKlUamwfOXJkvo+VmJiIy5cvq9cjIyNx6tQpODo6okKFCggICMCtW7ewaNEiAMD06dPh5eWFWrVqITU1FUuWLEFwcDCCg4N1vQwiIgJU0zgM3AiEbwC2TQQeXgPWDQWOzAU6BqpGfRIVMzr3ufH29s79YAoFrl7N4T0vucjtpXwDBgzAwoULMXDgQFy7dg27d+8GAPz444+YO3cubt26BSsrK9SqVQsBAQFa81zlxeDTLxARFVXpKcDhOUDoVCA1QVVW83XV6KvSXgYNjUivHYqLOyY3RETPkXgP2PUtcOIv1egqU3OgyVCgxSeAJX9vkmHotUMxEREZOduyQLfpqo7H3i2BjFRg/3TgtwbA8b+AzAxDR0iUpxdqubl58ybWr1+PqKgopKamamz7+eefCyw4fWDLDRGRDkSAiyHAlgmqlwECgEsdoON3gHfO7xcj0ge9vqF4x44d6N69O7y9vREREYHatWvj2rVrEBE0aNDghYMmIqIiSKFQTcZZqQ1w9E8g9Hvgzlngr25A9a6q/jhlKhk6SiINOj+WCggIwCeffIJz587B0tISwcHBuHHjBlq2bFmk3n9DREQFyMwc8BsKjDgJNBoCKExVbz6e+YqqVSf5kaEjJFLTObkJDw/HgAEDAABmZmZITk6Gra0tvvrqK/zwww8FHiARERUhNmWALj8BHx0AKrcFMtOAg7+r+uMc/RPISDd0hES6Jzc2Njbq6QzKlSuHK1euqLfdv3+/4CIjIqKiy7k68F4w0HcV4FQNePwA+PcTYE5z4PIOQ0dHJZzOyU2TJk2wf/9+AECXLl3wySef4Ntvv8X777+PJk2aFHiARERUhFVpB3y0H+g0FbAqDdwLB5a8ASx9C7h30dDRUQml82ipq1evIjExEXXr1sXjx48xduxY7Nu3D5UrV8Yvv/wCT09PfcVaIDhaiohIT5IfAqE/qt5unJkOmJgBjT4AWo4HrB0NHR0Vc3yJXx6Y3BAR6dn9S8DWL1RDyAHAshTQ+nOg4fuAqTLPXYlyo9eX+FWsWBEPHjzQKn/06BEqVqyo6+GIiMjYOFUB3l0B9FsDONcEnjwCNn8KzG4KXNpm6OioBNA5ubl27RoyMrTfTpmSkoJbt24VSFBERGQEKr0GfLgX6PIzYF0GuH8RWPomsPgN4G64oaMjI5bvl/itX79e/f8tW7bAwcFBvZ6RkYEdO3bAy8urQIMjIqJiztQMaDQYqN0L2PsTcGgOcGUHMHs30HAQ0Opz1fByogKU7z43JiaqRh6FQoFnd1EqlfDy8sK0adPQtWvXgo+yALHPDRGRAT24Amz7UvUCQACwcABajVe9GNDM3LCxUZGm1w7F3t7eOHr0KJycnF4qSENhckNEVARE7gFCPldN5QAAjpWA9t+opnpQKAwbGxVJHC2VByY3RERFRGYGcGopsONrIOmuqsy7JdDhO8C1tmFjoyJHL6OlDh8+jM2bN2uULVq0CN7e3nB2dsb//vc/9ZuLiYiInsvEFGjQHxhxHGg+GjC1ACJDgT9aABtGAYn3DB0hFVP5Tm4mT56MM2fOqNfPnj2LwYMHo23btvjss8+wYcMGBAYG6iVIIiIyYpb2QNvJwPAjQM0egGQCxxcCM+oD+6YD6fzDmXST7+Tm1KlTaNOmjXp9+fLleOWVVzBv3jyMGTMGM2bMwMqVK/USJBERlQClvYC3/gIGbQbc6gGpCcD2ScDMxkDYOqBk9aKgl5Dv5Obhw4dwcXFRr4eGhqJjx47q9UaNGuHGjRsFGx0REZU8nk2BIbuAHrMBW1fg4TVgZX9gYRfg9ilDR0fFQL6TGxcXF0RGRgIAUlNTceLECfj5+am3JyQkQKnka7WJiKgAmJgA9d5V9cd59VPAzBK4vh+Y2wpYOwxIiDF0hFSE5Tu56dixIz777DPs3bsXAQEBsLa2RosWLdTbz5w5g0qVKuklSCIiKqEsbIHXJgDDjwF1egMQ4NQSYEYDYM9PQFqyoSOkIijfyc0333wDU1NTtGzZEvPmzcO8efNgbv70hUsLFixA+/bt9RIkERGVcKU8gF5/AoO3AeUbAmlJwM6vgd8bAeeC2R+HNOj8npu4uDjY2trC1NRUozw2Nha2trYaCU9RxPfcEBEVc5mZqoRm+yQg/r85Dat0AHrNAywd8t6Xii29zgru4OCgldgAgKOjY5FPbIiIyAiYmAB1e6seVbWeoOqPc2kLMO814N5FQ0dHRYDOyQ0REVGRYG4NtPwUeD8EsHcHHlxWJTgRm5+/LxWohNQExCTl3Mk7JikGCakJhRoPkxsiIireytUH/rcb8GymejfO328DoT+qHl+R3iWkJsB/uz8GhQzSSnBikmIwKGQQ/Lf7F2qCw+SGiIiKP9uyQP91qtnFAWDXt8DKfkBK4bYYlERJaUmITY7FzcSbGglOVmJzM/EmYpNjkZSWVGgxMbkhIiLjYKoEuvwEdP8NMDUHLmwE/mwLPLhi6MiMmquNK4I6BsHd1l2d4Jy6e0qd2LjbuiOoYxBcbVwLLSbOCk5ERMbnxlFgxXtAYoxqBFWvBUCVtoaOyqhlb6nJUpCJjV5HSxERERV5Ho2AD0MB98bAkzhgWW/VJJwl6+/5QuVq44rAFpoTaAe2CCzUFpssTG6IiMg42bkCAzcCDfqrZhrfPglY9T6QWnh9P0qSmKQYBOwN0CgL2BuQ6ygqfWJyQ0RExsvMAug2A+gyDTAxA86vBuZ3AB5eN3RkRiX7Iyl3W3cs7rRYow9OYSc4TG6IiMi4KRRAow+AARsAm7LAnbOqCTivhho6MqPwbGIT1DEI9ZzraXUyLswEh8kNERGVDJ5NVe/DcasHJMcCi3sCB2exH85LslHawNHKUavzcPZRVI5WjrBR2hRaTBwtRUREJUtaMrDhY+DMctW6zztA118ApZVBwyrOElITkJSWlGPn4ZikGNgobWBnbvdS5+BoKSIiotworYCec4CO3wMKU+D030BQJyDu5vP3pRzZmdvlOirK1cb1pRMbXRk0udmzZw+6deuGcuXKQaFQYO3atc/dJzQ0FL6+vrC0tETFihUxZ84c/QdKRETGRaEAmnwE9FsNWDkCt0+q+uFcP2DoyKgAGDS5SUpKgo+PD37//fd81Y+MjETnzp3RokULnDx5Ep9//jlGjhyJ4OBgPUdKRERGqWIr4H+7AJc6QNI94K9uwNE/2Q+nmCsyfW4UCgXWrFmDHj165Fpn/PjxWL9+PcLDw9Vl/v7+OH36NA4ePJiv87DPDRERaUlNAtYNVw0VB1Tvxun8k2ooORUJRtvn5uDBg2jfvr1GWYcOHXDs2DGkpaXluE9KSgri4+M1FiIiIg3mNsCbC4C2UwAogBOLgIVdgPhoQ0dGL6BYJTcxMTFwcXHRKHNxcUF6ejru37+f4z6BgYFwcHBQLx4eHoURKhERFTcKBdD8Y6DvKtV8VDePqvrh3Dhq6MhIR8UquQFUj6+yy3qq9mx5loCAAMTFxamXGzdu6D1GIiIqxqq0BYbsAsrWUE28ubAzcGKxoaMiHRSr5MbV1RUxMZpvOLx79y7MzMxQpkyZHPexsLCAvb29xkJERJSnMpWAD7YB1bsCGanA+uHAv2OBjJy7QFDRUqySGz8/P2zbtk2jbOvWrWjYsCGUSqWBoiIiIqNkYQe8tRhoPUG1fnQesOh1IPGeYeOi5zJocpOYmIhTp07h1KlTAFRDvU+dOoWoqCgAqkdK/fv3V9f39/fH9evXMWbMGISHh2PBggWYP38+xo4da4jwiYjI2JmYAC0/Bd7+GzC3A67vV/XDuX3S0JFRHgya3Bw7dgz169dH/fr1AQBjxoxB/fr18eWXXwIAoqOj1YkOAHh7e2PTpk3YvXs36tWrh6+//hozZsxAr169DBI/ERGVENU7A0N2AmUqA/E3gQUdgdMrDB0V5aLIvOemsPA9N0RE9MKexAHBQ4BLW1TrfsNVw8dNzQwbVwlgtO+5ISIiMihLB+Cd5UCL/7pDHPwdWPIG8DjWsHGRBiY3REREujAxAdpMBHr/BShtgMhQYG5LIOasoSOj/zC5ISIiehG1eqiGi5f2Ah5FAfPbA+dWGzoqApMbIiKiF+dSS/XCv4qtgbTHwKpBwPbJQGaGoSMr0ZjcEBERvQxrR9WUDU1Hqtb3/QIsewtIfmjYuEowJjdEREQvy9QMaP810Gs+YGYFXN4OzHsNuHvB0JGVSExuiIiICkqdN4HBWwAHDyD2KvBnGyB8o6GjKnGY3BARERUkNx/gf7sBrxZAaiKwoi+wKxDIzDR0ZCUGkxsiIqKCZuME9FsDvPKRaj30e1WS8yTesHGVEExuiIiI9MFUCXT6Hnh9FmBqAURsAv5sC9y/bOjIjB6TGyIiIn2q3xcYtBmwKwfcj1B1NL641dBRGTUmN0RERPrm7qvqh+PxCpASpxoqvncaULKmdyw0nOkrFxkZGUhLSzN0GEQGZ25uDhMT/h1E9NLsXIABG4HNnwLHg4AdXwHRp1WPrSxsDR2dUWFy8wwRQUxMDB49emToUIiKBBMTE3h7e8Pc3NzQoRAVf2bmQLfpgFtdYNOnQNg6VR+ct5cCjt6Gjs5oKERKVpvY86ZMj46OxqNHj+Ds7Axra2soFAoDRElUNGRmZuL27dtQKpWoUKECfx6IClLUIWBFPyDpLmBZCugdBFR6zdBRFVnP+/zOji032WRkZKgTmzJlyhg6HKIioWzZsrh9+zbS09OhVCoNHQ6R8ajQBPgwFFjxHnDrOLCkF9DpR6DxEENHVuzxQXo2WX1srK2tDRwJUdGR9TgqI4MTARIVOPtywMBNQL2+gGQCm8YCJ5cYOqpij8lNDtj0TvQUfx6I9ExpCbw+E2g6QrW+foSqLw69MCY3REREhqZQAO2+Bhr0V7XgBH8AXNlp6KiKLSY3RERERYFCAXSdDtTsAWSkAsv7AjeOGDqqYonJjZEYOHAgFAqFeilTpgw6duyIM2fOGDo0IiLKLxNT4I15QKU2QNpjYOmbQMw5Q0dV7DC5MSIdO3ZEdHQ0oqOjsWPHDpiZmaFr166GDouIiHRhZg70WQx4NAGexAGLewIPrhg6qmKFyc1ziAgep6YbZNH1FUQWFhZwdXWFq6sr6tWrh/Hjx+PGjRu4d+8eAGD8+PGoWrUqrK2tUbFiRUycOFHjLcynT59G69atYWdnB3t7e/j6+uLYsWPq7QcOHMCrr74KKysreHh4YOTIkUhKSiqYG01ERE+Z2wDvrgBc6qjeg7OoBxB3y9BRFRt8z81zJKdloOaXWwxy7rCvOsDa/MW+RImJiVi6dCkqV66sfmePnZ0dFi5ciHLlyuHs2bMYMmQI7Ozs8OmnnwIA+vbti/r162P27NkwNTXFqVOn1O81OXv2LDp06ICvv/4a8+fPx7179zB8+HAMHz4cQUFBBXPBRET0lFUpoN9qYEFHIPaKqgVn0GbAhu9hex6+oTibJ0+eIDIyEt7e3rC0tAQAPE5NLxbJzcCBA7FkyRJ13ElJSXBzc8PGjRvRoEGDHPeZOnUqVqxYoW6dsbe3x2+//YYBAwZo1e3fvz+srKzwxx9/qMv27duHli1bIikpSX1eMj45/VwQUSF6dANY0AGIvwW41QMGbAAs835DrzHiG4oLkJXSFGFfdTDYuXXRunVrzJ49GwAQGxuLWbNmoVOnTjhy5Ag8PT2xatUqTJ8+HZcvX0ZiYiLS09M1vkHGjBmDDz74AIsXL0bbtm3Ru3dvVKpUCQBw/PhxXL58GUuXLlXXFxFkZmYiMjISNWrUKIArJiIiLaU8gH5rgaCOQPQp4O93gPdWAUorQ0dWZDG5eQ6FQvHCj4YKm42NDSpXrqxe9/X1hYODA+bNm4euXbvi7bffxpQpU9ChQwc4ODhg+fLlmDZtmrr+5MmT8e677+Lff//F5s2bMWnSJCxfvhw9e/ZEZmYmPvzwQ4wcOVLrvBUqVCiU6yMiKrHKVgXeWw381Q24vg/4ZyDQZwlgyilRclI8PrXphSgUCpiYmCA5ORn79++Hp6cnJkyYoN5+/fp1rX2qVq2KqlWrYvTo0XjnnXcQFBSEnj17okGDBjh//rxG8kRERIWoXD3gneXAkjeAiyHA2o+AnnMBE44NehbviBFJSUlBTEwMYmJiEB4ejhEjRiAxMRHdunVD5cqVERUVheXLl+PKlSuYMWMG1qxZo943OTkZw4cPx+7du3H9+nXs378fR48eVT9uGj9+PA4ePIhhw4bh1KlTuHTpEtavX48RI0YY6nKJiEoer2bAW4sBEzPg7D/A5nFAyeo6my9suTEiISEhcHNzA6AaGVW9enX8888/aNWqFQBg9OjRGD58OFJSUtClSxdMnDgRkydPBgCYmpriwYMH6N+/P+7cuQMnJye88cYbmDJlCgCgbt26CA0NxYQJE9CiRQuICCpVqoQ+ffoY4lKJiEququ2Bnn+opmg4+idg6QC0+dLQURUpHC2VDUeFEGnjzwVREXUsCNj4ser/7b4Gmmn3iTQmuoyW4mMpIiKi4qjhIKDtZNX/t00Ejv9l0HCKEiY3RERExVXz0UCzj1X/3zAKOL8mz+olBZMbIiKi4qztZMB3EAABgocAl7YbOiKDY3JDRERUnCkUQJdpQK03gMw0YMV7QNQhQ0dlUAZPbmbNmqXuqOjr64u9e/fmWnf37t1QKBRay4ULFwoxYiIioiLGxFQ1gqpKeyA9GVj6FhB9xtBRGYxBk5sVK1bg448/xoQJE3Dy5Em0aNECnTp1QlRUVJ77RUREIDo6Wr1UqVKlkCImIiIqoszMgd5/ARWaAilxqok27182dFQGYdDk5ueff8bgwYPxwQcfoEaNGpg+fTo8PDzU8yPlxtnZGa6ururF1FS3OZiIiIiMkrk18O5ywM0HeHwfWPQ6EHfT0FEVOoMlN6mpqTh+/Djat2+vUd6+fXscOHAgz33r168PNzc3tGnTBrt27cqzbkpKCuLj4zUWIiIio2XpoJqHqkwVIP4msKgHkHjP0FEVKoMlN/fv30dGRgZcXFw0yl1cXBATE5PjPm5ubpg7dy6Cg4OxevVqVKtWDW3atMGePXtyPU9gYCAcHBzUi4eHR4FeB1Fhyepz9ujRI0OHQkRFnY0T0H8t4OABPLikmo/qSZyhoyo0Bu9QrFAoNNZFRKssS7Vq1TBkyBA0aNAAfn5+mDVrFrp06YKffvop1+MHBAQgLi5Ovdy4caNA4y9KYmJiMGLECFSsWBEWFhbw8PBAt27dsGPHDkOHZlCTJ09Wdz43MzODk5MTXn31VUyfPh0pKSmGDs+grl27BoVCgVOnThk6FCIqaA7uQL+1gE1ZIOYMsOxtIPWxoaMqFAZLbpycnGBqaqrVSnP37l2t1py8NGnSBJcuXcp1u4WFBezt7TUWY3Tt2jX4+vpi586d+PHHH3H27FmEhISgdevWGDZsmEFjS01NNej5AaBWrVqIjo5GVFQUdu3ahd69eyMwMBBNmzZFQkKCocMjItIPp8qqR1QWDkDUAWBlfyDd8L+T9c1gyY25uTl8fX2xbds2jfJt27ahadOm+T7OyZMn1ZNFlmRDhw6FQqHAkSNH8Oabb6Jq1aqoVasWxowZg0OHnr7vICoqCq+//jpsbW1hb2+Pt956C3fu3AGgGoWW09D6n3/+GV5eXsiahiwsLAydO3eGra0tXFxc0K9fP9y/f19dv1WrVhg+fDjGjBkDJycntGvXTn2cOnXqwMbGBh4eHhg6dCgSExM1zjVv3jx4eHjA2toaPXv2xM8//4xSpUpp1NmwYQN8fX1haWmJihUrYsqUKUhPT8/z/piZmcHV1RXlypVDnTp1MGLECISGhuLcuXP44Ycf1PVSU1Px6aefonz58rCxscErr7yC3bt3q7cvXLgQpUqVwtq1a1G1alVYWlqiXbt2Wi2Cz4tRoVDgzz//RM+ePWFtbY0qVapg/fr1GsfYtGkTqlatCisrK7Ru3RrXrl3Tuq4DBw7g1VdfhZWVFTw8PDBy5EgkJSWpt3t5eeG7777D+++/Dzs7O1SoUAFz585Vb/f29gag6semUCjUk6wSkRFxqwv0XQmYWQGXtwFrPgQyMwwdlX6JAS1fvlyUSqXMnz9fwsLC5OOPPxYbGxu5du2aiIh89tln0q9fP3X9X375RdasWSMXL16Uc+fOyWeffSYAJDg4ON/njIuLEwASFxentS05OVnCwsIkOTn5aWFmpkhKomGWzMx8XdODBw9EoVDId999l2e9zMxMqV+/vjRv3lyOHTsmhw4dkgYNGkjLli3VdXx9feWLL77Q2M/X11cCAgJEROT27dvi5OQkAQEBEh4eLidOnJB27dpJ69at1fVbtmwptra2Mm7cOLlw4YKEh4eLiOrrt3PnTrl69ars2LFDqlWrJh999JF6v3379omJiYlMnTpVIiIiZObMmeLo6CgODg7qOiEhIWJvby8LFy6UK1euyNatW8XLy0smT56c63VPmjRJfHx8ctz2+uuvS40aNdTr7777rjRt2lT27Nkjly9flqlTp4qFhYVcvHhRRESCgoJEqVRKw4YN5cCBA3Ls2DFp3LixNG3aVKcYAYi7u7ssW7ZMLl26JCNHjhRbW1t58OCBiIhERUWJhYWFjBo1Si5cuCBLliwRFxcXASAPHz4UEZEzZ86Ira2t/PLLL3Lx4kXZv3+/1K9fXwYOHKg+j6enpzg6OsrMmTPl0qVLEhgYKCYmJuqvyZEjRwSAbN++XaKjo9Xnzy7HnwsiKn4ubROZUkZkkr3I+pH5/owpKvL6/H6WQZMbEZGZM2eKp6enmJubS4MGDSQ0NFS9bcCAARofvD/88INUqlRJLC0tpXTp0tK8eXP5999/dTqfzslNSqLqG8EQS0pivq7p8OHDAkBWr16dZ72tW7eKqampREVFqcvOnz8vAOTIkSMiIvLzzz9LxYoV1dsjIiIEgJw/f15ERCZOnCjt27fXOO6NGzcEgERERIiIKrmpV6/ec+NeuXKllClTRr3ep08f6dKli0advn37aiQ3LVq00EriFi9eLG5ubrmeJ6/kZvz48WJlZSUiIpcvXxaFQiG3bt3SqNOmTRt1chcUFCQA5NChQ+rt4eHhAkAOHz6c7xgBaCSRiYmJolAoZPPmzSIiEhAQIDVq1JDMbL98xo8fr5Hc9OvXT/73v/9pnGfv3r1iYmKi/h729PSU9957T709MzNTnJ2dZfbs2SIiEhkZKQDk5MmTOd4fESY3REbl3GqRyaVUnzFbvzR0NDrRJbkxK/SmomcMHToUQ4cOzXHbwoULNdY//fRTfPrpp4UQVfEi/z0uyq0jdpbw8HB4eHhojBirWbMmSpUqhfDwcDRq1Ahvv/02xo0bh0OHDqFJkyZYunQp6tWrh5o1awIAjh8/jl27dsHW1lbr+FeuXEHVqlUBAA0bNtTavmvXLnz33XcICwtDfHw80tPT8eTJEyQlJcHGxgYRERHo2bOnxj6NGzfGxo0b1evHjx/H0aNH8e2336rLMjIy8OTJEzx+/BjW1tbPu10aJFsH9hMnTkBE1NeQJSUlBWXKlFGvm5mZaVxf9erV1fewcePG+Y6xbt266u02Njaws7PD3bt3Aai+Vk2aNNH4mvr5+WnEdfz4cVy+fBlLly7VuJ7MzExERkaiRo0aWudRKBRwdXVVn4eISphaPYEn8cCGkcD+6YBVKdXkm0bG4MlNkae0Bj6/bbhz50OVKlWgUCgQHh6OHj165FpPchmJlr3czc0NrVu3xrJly9CkSRP8/fff+PDDD9V1MzMz0a1bN41+Klmy932ysbHR2Hb9+nV07twZ/v7++Prrr+Ho6Ih9+/Zh8ODBSEtLyzW+rMQt+/mnTJmCN954Q+v8lpaWuV57bsLDw9X9TjIzM2Fqaorjx49rvRjy2WQup/uYVZbfGJVKpdb+mZmZALSvOyeZmZn48MMPMXLkSK1tFSpUyNd5iKgE8h2gGha+bSKwfbLqvTgN3zd0VAWKyc3zKBSAuc3z6xmQo6MjOnTogJkzZ2LkyJFaicWjR49QqlQp1KxZE1FRUbhx44a69SYsLAxxcXHqv/IBoG/fvhg/fjzeeecdXLlyBW+//bZ6W4MGDRAcHAwvLy+YmeX/2+fYsWNIT0/HtGnTYGKi6se+cuVKjTrVq1fHkSNHtPbLrkGDBoiIiEDlypXzfe7cXLhwASEhIQgICACg6lSbkZGBu3fvokWLFrnul56ejmPHjqFx48YAVB2xHz16hOrVqxdYjDVr1sTatWs1yrJ3DM86z/nz51/qPObm5gBULUtEVII0Gwk8eQTsnQZsHANY2AN13jR0VAXG4O+5oYIxa9YsZGRkoHHjxggODsalS5cQHh6OGTNmqB9ntG3bFnXr1kXfvn1x4sQJHDlyBP3790fLli01HrO88cYbiI+Px0cffYTWrVujfPny6m3Dhg1DbGws3nnnHRw5cgRXr17F1q1b8f777+f5AVmpUiWkp6fjt99+w9WrV7F48WLMmTNHo86IESOwadMm/Pzzz7h06RL++OMPbN68WaOV5Msvv8SiRYswefJknD9/HuHh4VixYgW++OKLPO9Peno6YmJicPv2bZw9exa//fYbWrZsiXr16mHcuHEAgKpVq6Jv377o378/Vq9ejcjISBw9ehQ//PADNm3apD6WUqnEiBEjcPjwYZw4cQKDBg1CkyZN1MnOi8aYnb+/P65cuYIxY8YgIiICy5Yt03pMO378eBw8eBDDhg3DqVOncOnSJaxfvx4jRozI93mcnZ1hZWWFkJAQ3LlzB3FxJeclX0Ql3msTgUYfABDVCKqLWwwdUcHRX9efoknnDsXFyO3bt2XYsGHqDtrly5eX7t27y65du9R1rl+/Lt27dxcbGxuxs7OT3r17S0xMjNaxevfuLQBkwYIFWtsuXrwoPXv2lFKlSomVlZVUr15dPv74Y3Xn15YtW8qoUaO09vv555/Fzc1NrKyspEOHDrJo0SKNDrIiInPnzpXy5cuLlZWV9OjRQ7755htxdXXVOE5ISIg0bdpUrKysxN7eXho3bixz587N9b5MmjRJAAgAMTU1FUdHR2nevLn88ssv8uTJE426qamp8uWXX4qXl5colUpxdXWVnj17ypkzZ0RE1aHYwcFBgoODpWLFimJubi6vvfaaeoRffmMEIGvWrNHYx8HBQYKCgtTrGzZskMqVK4uFhYW0aNFCFixYoHW/jhw5Iu3atRNbW1uxsbGRunXryrfffqve7unpKb/88ovGeXx8fGTSpEnq9Xnz5omHh4eYmJhodODPUtx/LogoDxkZIqs+UHUw/tpZJHKfoSPKlS4dihUi+Xi4b0Ti4+Ph4OCAuLg4rRf6PXnyBJGRkfD29n6h/htU8IYMGYILFy5g7969hg4FgKqT+8cff1yipkDgzwWRkctIA1b0Ay5uVj2eGrABKFfP0FFpyevz+1l8LEVFyk8//YTTp0/j8uXL+O233/DXX39hwIABhg6LiMh4mSqB3kGAZ3MgJV41D9W9i4aO6qUwuaEi5ciRI2jXrh3q1KmDOXPmYMaMGfjggw8MHRYRkXFTWgHv/A2Uqw88fgAs7gE8ijJ0VC+Mj6WyYfM7kTb+XBCVIEkPgKBOwP0IwLES8H4IYOts6KgA8LEUERERvQibMkD/tUCpCkDsFWDxG0DyI0NHpTMmN0RERPSUfTmg31rAxhm4cxZY9haQmvTc3YoSJjdERESkqUwloN8a1duLbxxWjaZKTzV0VPnG5IaIiIi0udYG+q5STQV0ZQewegiQWTzeZs7khoiIiHLm0Rh4eylgag6ErQU2jAKKwTgkJjdERESUu0qvAb3mAwoT4ORi1YSbRTzBYXJDREREeavZHej+m+r/B35TTbhZhDG5MRIDBw6EQqGAQqGAUqmEi4sL2rVrhwULFiAzM1Ndz8vLS10va3F3d8fkyZO1yp9drl27ZrgLJCIiw6r/HtAhUPX/nV8DR+YZNp48MLkxIh07dkR0dDSuXbuGzZs3o3Xr1hg1ahS6du2K9PR0db2vvvoK0dHR6uXkyZMYO3asRpm7u7tWPQ8PDwNeHRERGZzfUKDleNX/N40Fzqw0bDy5MDN0AMYmITUBSWlJcLVx1doWkxQDG6UN7Mzt9HJuCwsLuLqqzlu+fHk0aNAATZo0QZs2bbBw4UL1NAZ2dnbqetnZ2tqq/29qapprPSIiKsFaBahe7HfkD2CNP2BhB1TrZOioNLDlpgAlpCbAf7s/BoUMQkxSjMa2mKQYDAoZBP/t/khITSi0mF577TX4+Phg9erVhXZOIiIyYgoF0PF7wOcdQDKAlQOAyL2GjkoDk5sClJSWhNjkWNxMvKmR4GQlNjcTbyI2ORZJaYX7psfq1atr9JcZP348bG1t1cuMGTMKNR4iIirmTEyA7r8D1boAGSnA328Dt04YOio1JjcFyNXGFUEdg+Bu665OcE7dPaVObNxt3RHUMSjHR1b6JCJQKBTq9XHjxuHUqVPqpX///oUaDxERGQFTM+DNBYD3q0BqIrCkF3D3gqGjAsDkpsA9m+D029zPoIkNAISHh8Pb21u97uTkhMqVK6uXUqVKFXpMRERkBJSWwNvLgPK+QHIssLgH8PCaoaNicqMPrjauCGwRqFEW2CLQIInNzp07cfbsWfTq1avQz01ERCWAhZ1qmoayNYCEaGBRDyAh5rm76ROTGz2ISYpBwN4AjbKAvQFanYwLWkpKCmJiYnDr1i2cOHEC3333HV5//XV07dqVj56IiEh/rB1VE22W9gIeRgKL3wCSHxosHCY3BSx752F3W3cs7rRYow+OPhOckJAQuLm5wcvLCx07dsSuXbswY8YMrFu3Dqampno7LxEREezdgH5rAVtXwM5FNR+VgShEivgEEQUsPj4eDg4OiIuLg729vca2J0+eIDIyEt7e3rC0tNT52M8mNll9bHIrJyoOXvbngohKmNhIwL4cYGZRoIfN6/P7WWy5KUA2Shs4WjlqJTDZOxk7WjnCRmlj4EiJiIj0xNG7wBMbXfENxQXIztwOc9rOyfENxVkJjj7fUExERERMbgqcnbldrskLH0URERHpHx9LERERkVFhckNERERGhclNDkrYADKiPPHngYiKGyY32SiVSgDA48ePDRwJUdGRmpoKAHxXEhEVG+xQnI2pqSlKlSqFu3fvAgCsra01JpwkKmkyMzNx7949WFtbw8yMvy6IqHjgb6tnuLqqRjRlJThEJZ2JiQkqVKjARJ+Iig2DJzezZs3C1KlTER0djVq1amH69Olo0aJFrvVDQ0MxZswYnD9/HuXKlcOnn34Kf3//AotHoVDAzc0Nzs7OSEtLK7DjEhVX5ubmMDHhE2wiKj4MmtysWLECH3/8MWbNmoVmzZrhjz/+QKdOnRAWFoYKFSpo1Y+MjETnzp0xZMgQLFmyBPv378fQoUNRtmzZAp/12tTUlH0MiIiIiiGDzi31yiuvoEGDBpg9e7a6rEaNGujRowcCAwO16o8fPx7r169HeHi4uszf3x+nT5/GwYMH83VOXeamICIioqKhWMwtlZqaiuPHj6N9+/Ya5e3bt8eBAwdy3OfgwYNa9Tt06IBjx47xERIREREBMOBjqfv37yMjIwMuLi4a5S4uLoiJiclxn5iYmBzrp6en4/79+3Bzc9PaJyUlBSkpKer1+Pj4AoieiIiIiiqDdyh+dgSGiOQ5KiOn+jmVZwkMDMSUKVO0ypnkEBERFR9Zn9v56U1jsOTGyckJpqamWq00d+/e1WqdyeLq6ppjfTMzM5QpUybHfQICAjBmzBj1+q1bt1CzZk14eHi85BUQERFRYUtISICDg0OedQyW3Jibm8PX1xfbtm1Dz5491eXbtm3D66+/nuM+fn5+2LBhg0bZ1q1b0bBhQ/XbhZ9lYWEBCwsL9bqtrS1u3LgBOzu7An9vR3x8PDw8PHDjxg12VtYj3ufCwftcOHifCw/vdeHQ130WESQkJKBcuXLPrWvQx1JjxoxBv3790LBhQ/j5+WHu3LmIiopSv7cmICAAt27dwqJFiwCoRkb9/vvvGDNmDIYMGYKDBw9i/vz5+Pvvv/N9ThMTE7i7u+vlerLY29vzB6cQ8D4XDt7nwsH7XHh4rwuHPu7z81psshg0uenTpw8ePHiAr776CtHR0ahduzY2bdoET09PAEB0dDSioqLU9b29vbFp0yaMHj0aM2fORLly5TBjxowCf8cNERERFV8Gfc+NseE7dAoH73Ph4H0uHLzPhYf3unAUhfvMd6oXIAsLC0yaNEmjjw8VPN7nwsH7XDh4nwsP73XhKAr3mS03REREZFTYckNERERGhckNERERGRUmN0RERGRUmNwQERGRUWFyo6NZs2bB29sblpaW8PX1xd69e/OsHxoaCl9fX1haWqJixYqYM2dOIUVavOlyn1evXo127dqhbNmysLe3h5+fH7Zs2VKI0RZfun4/Z9m/fz/MzMxQr149/QZoJHS9zykpKZgwYQI8PT1hYWGBSpUqYcGCBYUUbfGl631eunQpfHx8YG1tDTc3NwwaNAgPHjwopGiLpz179qBbt24oV64cFAoF1q5d+9x9DPI5KJRvy5cvF6VSKfPmzZOwsDAZNWqU2NjYyPXr13Osf/XqVbG2tpZRo0ZJWFiYzJs3T5RKpaxataqQIy9edL3Po0aNkh9++EGOHDkiFy9elICAAFEqlXLixIlCjrx40fU+Z3n06JFUrFhR2rdvLz4+PoUTbDH2Ive5e/fu8sorr8i2bdskMjJSDh8+LPv37y/EqIsfXe/z3r17xcTERH799Ve5evWq7N27V2rVqiU9evQo5MiLl02bNsmECRMkODhYAMiaNWvyrG+oz0EmNzpo3Lix+Pv7a5RVr15dPvvssxzrf/rpp1K9enWNsg8//FCaNGmitxiNga73OSc1a9aUKVOmFHRoRuVF73OfPn3kiy++kEmTJjG5yQdd7/PmzZvFwcFBHjx4UBjhGQ1d7/PUqVOlYsWKGmUzZswQd3d3vcVobPKT3Bjqc5CPpfIpNTUVx48fR/v27TXK27dvjwMHDuS4z8GDB7Xqd+jQAceOHUNaWpreYi3OXuQ+PyszMxMJCQlwdHTUR4hG4UXvc1BQEK5cuYJJkybpO0Sj8CL3ef369WjYsCF+/PFHlC9fHlWrVsXYsWORnJxcGCEXSy9yn5s2bYqbN29i06ZNEBHcuXMHq1atQpcuXQoj5BLDUJ+DBp1bqji5f/8+MjIy4OLiolHu4uKCmJiYHPeJiYnJsX56ejru378PNzc3vcVbXL3IfX7WtGnTkJSUhLfeeksfIRqFF7nPly5dwmeffYa9e/fCzIy/OvLjRe7z1atXsW/fPlhaWmLNmjW4f/8+hg4ditjYWPa7ycWL3OemTZti6dKl6NOnD548eYL09HR0794dv/32W2GEXGIY6nOQLTc6UigUGusiolX2vPo5lZMmXe9zlr///huTJ0/GihUr4OzsrK/wjEZ+73NGRgbeffddTJkyBVWrVi2s8IyGLt/PmZmZUCgUWLp0KRo3bozOnTvj559/xsKFC9l68xy63OewsDCMHDkSX375JY4fP46QkBBERkbC39+/MEItUQzxOcg/v/LJyckJpqamWn8F3L17VysrzeLq6ppjfTMzM5QpU0ZvsRZnL3Kfs6xYsQKDBw/GP//8g7Zt2+ozzGJP1/uckJCAY8eO4eTJkxg+fDgA1YewiMDMzAxbt27Fa6+9ViixFycv8v3s5uaG8uXLw8HBQV1Wo0YNiAhu3ryJKlWq6DXm4uhF7nNgYCCaNWuGcePGAQDq1q0LGxsbtGjRAt988w1b1guIoT4H2XKTT+bm5vD19cW2bds0yrdt24amTZvmuI+fn59W/a1bt6Jhw4ZQKpV6i7U4e5H7DKhabAYOHIhly5bxmXk+6Hqf7e3tcfbsWZw6dUq9+Pv7o1q1ajh16hReeeWVwgq9WHmR7+dmzZrh9u3bSExMVJddvHgRJiYmcHd312u8xdWL3OfHjx/DxETzI9DU1BTA05YFenkG+xzUa3dlI5M11HD+/PkSFhYmH3/8sdjY2Mi1a9dEROSzzz6Tfv36qetnDYEbPXq0hIWFyfz58zkUPB90vc/Lli0TMzMzmTlzpkRHR6uXR48eGeoSigVd7/OzOFoqf3S9zwkJCeLu7i5vvvmmnD9/XkJDQ6VKlSrywQcfGOoSigVd73NQUJCYmZnJrFmz5MqVK7Jv3z5p2LChNG7c2FCXUCwkJCTIyZMn5eTJkwJAfv75Zzl58qR6yH1R+RxkcqOjmTNniqenp5ibm0uDBg0kNDRUvW3AgAHSsmVLjfq7d++W+vXri7m5uXh5ecns2bMLOeLiSZf73LJlSwGgtQwYMKDwAy9mdP1+zo7JTf7pep/Dw8Olbdu2YmVlJe7u7jJmzBh5/PhxIUdd/Oh6n2fMmCE1a9YUKysrcXNzk759+8rNmzcLOeriZdeuXXn+vi0qn4MKEba/ERERkfFgnxsiIiIyKkxuiIiIyKgwuSEiIiKjwuSGiIiIjAqTGyIiIjIqTG6IiIjIqDC5ISIiIqPC5IaIKB8uXLiAJk2awNLSEvXq1TN0OESUByY3RCVITEwMRowYgYoVK8LCwgIeHh7o1q0bduzYYejQirxJkybBxsYGEREROd6vjIwMNG3aFL169dIoj4uLg4eHB7744gt1WXBwMFq1agUHBwfY2tqibt26+OqrrxAbG6uuk5ycjEmTJqFatWqwsLCAk5MT3nzzTZw/f15/F0lkJJjcEJUQ165dg6+vL3bu3Ikff/wRZ8+eRUhICFq3bo1hw4YZNLbU1FSDnj8/rly5gubNm8PT0zPH2YxNTU3x119/ISQkBEuXLlWXjxgxAo6Ojvjyyy8BABMmTECfPn3QqFEjbN68GefOncO0adNw+vRpLF68GACQkpKCtm3bYsGCBfj6669x8eJFbNq0CRkZGXjllVdw6NChwrloouJK7xM8EFGR0KlTJylfvrwkJiZqbXv48KH6/9evX5fu3buLjY2N2NnZSe/evSUmJkZERC5cuCAAJDw8XGP/adOmiaenp2RmZoqIyPnz56VTp05iY2Mjzs7O8t5778m9e/fU9Vu2bCnDhg2T0aNHS5kyZeTVV19VH6d27dpibW0t7u7u8tFHH0lCQoLGuebOnSvu7u5iZWUlPXr0kGnTpomDg4NGnfXr10uDBg3EwsJCvL29ZfLkyZKWlpbrvcnIyJApU6ZI+fLlxdzcXHx8fGTz5s3q7XhmHp1Jkybleqxff/1VSpcuLbdu3ZK1a9eKUqmUkydPiojI4cOHBYBMnz49x32zvg7ff/+9KBQKOXXqlFacDRs2lJo1a6rvNRFpY3JDVAI8ePBAFAqFfPfdd3nWy8zMlPr160vz5s3l2LFjcujQIWnQoIHGRHi+vr7yxRdfaOzn6+srAQEBIiJy+/ZtcXJykoCAAAkPD5cTJ05Iu3btpHXr1ur6LVu2FFtbWxk3bpxcuHBBnSz98ssvsnPnTrl69ars2LFDqlWrJh999JF6v3379omJiYlMnTpVIiIiZObMmeLo6KiR3ISEhIi9vb0sXLhQrly5Ilu3bhUvLy+ZPHlyrtf9888/i729vfz9999y4cIF+fTTT0WpVMrFixdFRCQ6Olpq1aoln3zyiURHR2slXM/ew1atWkmbNm3E2dlZvv76a/W2kSNHiq2traSmpubxVRCpW7eutG/fPsdtS5cuFQDqhImItDG5ISoBsloMVq9enWe9rVu3iqmpqURFRanLzp8/LwDkyJEjIqJKBCpWrKjeHhERIQDk/PnzIiIyceJErQ/mGzduCACJiIgQEVVyU69evefGvXLlSilTpox6vU+fPtKlSxeNOn379tVIblq0aKGVxC1evFjc3NxyPU+5cuXk22+/1Shr1KiRDB06VL3u4+OTZ4tNduHh4QJA6tSpo9Fi1KlTJ6lbt+5z97e0tJRRo0bluO3EiRMCQFasWJGvWIhKIva5ISoBRAQAoFAo8qwXHh4ODw8PeHh4qMtq1qyJUqVKITw8HADw9ttv4/r16+p+H0uXLkW9evVQs2ZNAMDx48exa9cu2Nraqpfq1asDUPVbydKwYUOt8+/atQvt2rVD+fLlYWdnh/79++PBgwdISkoCAERERKBx48Ya+zy7fvz4cXz11Vca5x8yZAiio6Px+PFjrXPGx8fj9u3baNasmUZ5s2bN1NesqwULFsDa2hqRkZG4efOmulxEnvs1eJ78fi2JSjImN0QlQJUqVaBQKJ77YZ3bh2/2cjc3N7Ru3RrLli0DAPz9999477331HUzMzPRrVs3nDp1SmO5dOkSXn31VXU9GxsbjXNcv34dnTt3Ru3atREcHIzjx49j5syZAIC0tLRc48v6sM9+/ilTpmic++zZs7h06RIsLS1zvfacjvsiCcTBgwfxyy+/YN26dfDz88PgwYPVMVatWhVXrlxRX09uqlatirCwsBy3XbhwAYDqa0pEOWNyQ1QCODo6okOHDpg5c6a6FSS7R48eAVC10kRFReHGjRvqbWFhYYiLi0ONGjXUZX379sWKFStw8OBBXLlyBW+//bZ6W4MGDXD+/Hl4eXmhcuXKGsuzCU12x44dQ3p6OqZNm4YmTZqgatWquH37tkad6tWr48iRI1r7ZdegQQNERERonbty5cowMdH+lWdvb49y5cph3759GuUHDhzQuOb8SE5OxoABA/Dhhx+ibdu2+PPPP3H06FH88ccfAIB3330XiYmJmDVrVo77Z30d3n77bWzfvh2nT5/W2J6ZmYlffvkFNWvWhI+Pj06xEZUoBnsgRkSF6urVq+Lq6io1a9aUVatWycWLFyUsLEx+/fVXqV69uog87VDcokULOX78uBw+fFh8fX01OhSLiMTFxYmlpaX4+PhImzZtNLbdunVLypYtK2+++aYcPnxYrly5Ilu2bJFBgwZJenq6iKj63Dzbp+TkyZPqkURXrlyRRYsWSfny5QWAehRRVofiadOmycWLF2XOnDlSpkwZKVWqlPo4ISEhYmZmJpMmTZJz585JWFiYLF++XCZMmJDrvfnll1/E3t5eli9fLhcuXJDx48drdCgWyV+fm5EjR0qlSpU0RqTNnTtXbG1tJTIyUkREPv30UzE1NZVx48bJgQMH5Nq1a7J9+3Z588031aOokpOT5ZVXXhEPDw9ZuXKlXL9+XY4cOSI9evQQGxsbOXjwYJ5xEJV0TG6ISpDbt2/LsGHDxNPTU8zNzaV8+fLSvXt32bVrl7pOXkPBs+vdu7cAkAULFmhtu3jxovTs2VNKlSolVlZWUr16dfn444/Vw5dzSm5EVJ2V3dzcxMrKSjp06CCLFi3SSG5EVMlC+fLl1UPBv/nmG3F1ddU4TkhIiDRt2lSsrKzE3t5eGjduLHPnzs31vmQfCq5UKrWGgos8P7nZvXu3mJqayt69e7W2tW/fXl577TX19a9YsUJeffVVsbOzExsbG6lbt6589dVXGteZlJQkX3zxhVSuXFmUSqU4OjpKr1695OzZs7nGQEQqCpFnHlgTERUjQ4YMwYULF7B3715Dh0JERYSZoQMgItLFTz/9hHbt2sHGxgabN2/GX3/9lWsfFiIqmdhyQ0TFyltvvYXdu3cjISEBFStWxIgRI+Dv72/osIioCGFyQ0REREaFQ8GJiIjIqDC5ISIiIqPC5IaIiIiMCpMbIiIiMipMboiIiMioMLkhIiIio8LkhoiIiIwKkxsiIiIyKkxuiIiIyKj8H6MyPHQ7Wx1qAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(coverages, entropies_base, label='Base', color=colors[0])\n", + "plt.plot(coverages, entropies_cov_dep, label='Coverage Dependent', color=colors[1])\n", + "\n", + "# these values copied from Cantera example: https://cantera.org/dev/examples/python/thermo/coverage_dependent_surf.html\n", + "# provide discrete enthalpy and entropy values calculated with DFT\n", + "# array of CO* coverage\n", + "dft_covs = [0., 0.11, 0.22, 0.33, 0.44, 0.56, 0.67, 0.78, 0.89, 1.0]\n", + "\n", + "# array of nondimensionalized DFT-derived CO* entropy values\n", + "dft_srs = [2.46, 2.46, 3.79, 3.28, 3.14, 2.41, 2.19, 1.63, 1.6 , 1.12]\n", + "\n", + "plt.scatter(dft_covs, dft_srs, label='DFT', color=colors[2], marker='x')\n", + "\n", + "\n", + "plt.xlabel('Coverage of XCO')\n", + "plt.ylabel('Standard Entropy / R')\n", + "plt.title('Standard Entropy of XCO vs Coverage')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06312712", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a846327b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rmg_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.23" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 40e65623c140cfd4ec877b939c23163cde07622e Mon Sep 17 00:00:00 2001 From: Sevy Harris Date: Fri, 10 Apr 2026 09:36:20 -0400 Subject: [PATCH 2/2] explain entropy offset int cov-dep notebook Added some explanation to the coverage-dependence example jupyter notebook that the difference in entropy is explained by the difference in enthalpy between Jongyoon's calculations and Bjarne's --- ipython/coverage_dependent_thermo.ipynb | 3780 +++++++++++++++++++++-- 1 file changed, 3444 insertions(+), 336 deletions(-) diff --git a/ipython/coverage_dependent_thermo.ipynb b/ipython/coverage_dependent_thermo.ipynb index 7ceb64b41cb..9f49f387004 100644 --- a/ipython/coverage_dependent_thermo.ipynb +++ b/ipython/coverage_dependent_thermo.ipynb @@ -1,9 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "11c0b84e-3287-4231-b37b-f27486a6d2e1", + "metadata": {}, + "source": [ + "# Example running RMG with a thermo lib with coverage-dependence" + ] + }, { "cell_type": "code", - "execution_count": 2, - "id": "2635da2c", + "execution_count": 1, + "id": "81ea402f-9a0b-4b67-83ae-8c22175be431", "metadata": {}, "outputs": [], "source": [ @@ -22,24 +30,39 @@ "source": [ "# Run baseline and coverage-dependent RMG jobs\n", "\n", - "This should take less than 5 minutes" + "Each of the runs should take less than 2 minutes" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "4daa7653", + "execution_count": 2, + "id": "0705ecb7-7fe2-48a9-b168-4884859dbe3f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Running RMG with input file at temp/cov_dep_rmg_run/input.py...\n", + "Running RMG with input file at temp/base_rmg_run/input.py...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":root:Removing old /home/moon/rmg/RMG-Py/ipython/temp/base_rmg_run/RMG_backup.log\n", + ":root:Moving /home/moon/rmg/RMG-Py/ipython/temp/base_rmg_run/RMG.log to /home/moon/rmg/RMG-Py/ipython/temp/base_rmg_run/RMG_backup.log\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Global RMG Settings:\n", " database.directory = /home/moon/rmg/RMG-database/input (Default, relative to RMG-Py source code)\n", " test_data.directory = /home/moon/rmg/RMG-Py/test/rmgpy/test_data (Default, relative to RMG-Py source code)\n", - "RMG execution initiated at Tue Apr 7 16:44:57 2026\n", + "RMG execution initiated at Fri Apr 10 09:32:44 2026\n", "\n", "#########################################################\n", "# RMG-Py - Reaction Mechanism Generator in Python #\n", @@ -51,18 +74,18 @@ "#########################################################\n", "\n", "The current git HEAD for RMG-Py is:\n", - "\tb'5bbd2707ea570c5a4fb681f0baa071fb1058ad24'\n", - "\tb'Mon Apr 6 15:20:46 2026 -0400'\n", + "\tb'd376fd6fe0f4c0cea57bf879cc4c2745afbeb52d'\n", + "\tb'Fri Apr 10 08:01:23 2026 -0400'\n", "\n", "The current git HEAD for RMG-database is:\n", "\tb'edfb0e5d715146fd0edd2201a0749217781af2b3'\n", "\tb'Tue Apr 7 16:09:33 2026 -0400'\n", "\n", - "Reading input file \"/home/moon/rmg/RMG-Py/ipython/temp/cov_dep_rmg_run/input.py\"...\n", + "Reading input file \"/home/moon/rmg/RMG-Py/ipython/temp/base_rmg_run/input.py\"...\n", "\n", "# Data sources\n", "database(\n", - " thermoLibraries=['covDepSurfaceThermoPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", + " thermoLibraries=['surfaceThermoCovDepPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", " reactionLibraries = [\n", " ('Surface/Methane/Deutschmann_Pt', False),\n", " ('Surface/Methane/Vlachos_Pt111', False),\n", @@ -75,7 +98,7 @@ "\n", "catalystProperties( # default values for Pt(111)\n", " metal = 'Pt111',\n", - " thermoCoverageDependence = True,\n", + " thermoCoverageDependence = False,\n", ")\n", "\n", "# List of species\n", @@ -358,25 +381,7 @@ ")\n", "\n", "surfaceReactor(\n", - " temperature=(600,'K'),\n", - " initialPressure=(1.0, 'bar'),\n", - " initialGasMoleFractions={\n", - " \"CH4\": 0.108574,\n", - " \"O2\": 0.02088,\n", - " \"Ar\": 0.78547,\n", - " },\n", - " initialSurfaceCoverages={\n", - " \"X\": 1.0,\n", - " },\n", - " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", - " terminationConversion = { \"CH4\":0.95,},\n", - " terminationTime=(10., 's'),\n", - " # terminationConversion={'O2': 0.99,},\n", - " terminationRateRatio=0.05\n", - ")\n", - "\n", - "surfaceReactor(\n", - " temperature=(2000,'K'),\n", + " temperature=(1000,'K'),\n", " initialPressure=(1.0, 'bar'),\n", " initialGasMoleFractions={\n", " \"CH4\": 0.041866,\n", @@ -402,19 +407,9 @@ "model(\n", " toleranceKeepInEdge=0.0,\n", " toleranceMoveToCore=1e-1,\n", - "# inturrupt tolerance was 0.1 wout pruning, 1e8 w pruning on\n", " toleranceInterruptSimulation=1e8,\n", " maximumEdgeSpecies=500000,\n", - " maxNumSpecies=44,\n", - "# PRUNING: uncomment to prune\n", - "# minCoreSizeForPrune=50,\n", - "# prune before simulation based on thermo\n", - "# toleranceThermoKeepSpeciesInEdge=0.5,\n", - "# prune rxns from edge that dont move into core\n", - "# minSpeciesExistIterationsForPrune=2,\n", - "# FILTERING: set so threshold is slightly larger than max rate constants\n", - "# filterReactions=True,\n", - "# filterThreshold=5e8, # default value\n", + " maxNumSpecies=40,\n", ")\n", "\n", "options(\n", @@ -441,62 +436,81 @@ "\n", "Surface reaction system 1\n", "Gas phase mole fractions:\n", - " CH4 0.11867\n", - " O2 0.022822\n", - " Ar 0.85851\n", - "Total gas phase: 1 moles\n", - "Pressure: 1e+05 Pa\n", - "Temperature: 600.0 K\n", - "Reactor volume: 0.0499 m3\n", - "Surface/volume ratio: 1e+05 m2/m3\n", - "Surface site density: 2.48e-05 mol/m2\n", - "Surface sites in reactor: 0.124 moles\n", - "Initial surface coverages (and amounts):\n", - " X 1 = 0.12387 moles\n", - "Warning: Initial gas mole fractions do not sum to one; renormalizing.\n", - "\n", - "\n", - "Surface reaction system 2\n", - "Gas phase mole fractions:\n", " CH4 0.20129\n", " O2 0.1677\n", " Ar 0.63101\n", "Total gas phase: 1 moles\n", "Pressure: 1e+05 Pa\n", - "Temperature: 2000.0 K\n", - "Reactor volume: 0.166 m3\n", + "Temperature: 1000.0 K\n", + "Reactor volume: 0.0831 m3\n", "Surface/volume ratio: 1e+05 m2/m3\n", "Surface site density: 2.48e-05 mol/m2\n", - "Surface sites in reactor: 0.413 moles\n", + "Surface sites in reactor: 0.206 moles\n", "Initial surface coverages (and amounts):\n", - " X 1 = 0.4129 moles\n", + " X 1 = 0.20645 moles\n", "Warning: Generate Output HTML option was turned on. Note that this will slow down model generation.\n", "Warning: Edge species saving was turned on. This will slow down model generation for large simulations.\n", "\n", "Loading transport library from PrimaryTransportLibrary.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", "Loading transport library from OneDMinN2.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", - "Loading transport library from NOx2018.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from NOx2018.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Loading transport library from GRI-Mech.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", "Loading transport library from NIST_Fluorine.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", - "Loading transport group database from /home/moon/rmg/RMG-database/input/transport/groups...\n", + "Loading transport group database from /home/moon/rmg/RMG-database/input/transport/groups...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Loading kinetics library Surface/Methane/Deutschmann_Pt from /home/moon/rmg/RMG-database/input/kinetics/libraries/Surface/Methane/Deutschmann_Pt/reactions.py...\n", "Loading kinetics library Surface/Methane/Vlachos_Pt111 from /home/moon/rmg/RMG-database/input/kinetics/libraries/Surface/Methane/Vlachos_Pt111/reactions.py...\n", "Loading kinetics library BurkeH2O2inArHe from /home/moon/rmg/RMG-database/input/kinetics/libraries/BurkeH2O2inArHe/reactions.py...\n", - "Loading frequencies group database from /home/moon/rmg/RMG-database/input/statmech/groups...\n", + "Loading frequencies group database from /home/moon/rmg/RMG-database/input/statmech/groups...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Loading solvation thermodynamics group database from /home/moon/rmg/RMG-database/input/solvation/groups...\n", - "Loading thermodynamics library from covDepSurfaceThermoPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from surfaceThermoCovDepPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", "Loading thermodynamics library from surfaceThermoPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", "Loading thermodynamics library from primaryThermoLibrary.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", - "Loading thermodynamics library from thermo_DFT_CCSDTF12_BAC.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from thermo_DFT_CCSDTF12_BAC.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Loading thermodynamics library from DFT_QCI_thermo.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", - "Loading thermodynamics group database from /home/moon/rmg/RMG-database/input/thermo/groups...\n", + "Loading thermodynamics group database from /home/moon/rmg/RMG-database/input/thermo/groups...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Trimolecular reactions are turned on\n", - "Adding rate rules from training set in kinetics families...\n", + "Adding rate rules from training set in kinetics families...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Warning: Cphigh is above the theoretical CpInf value for ThermoData object\n", - "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), thermo_coverage_dependence={}, comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", + "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", "The thermo for this species is probably wrong! Setting CpInf = Cphigh for Entropy calculationat T = 2000.0 K...\n", "Warning: Cphigh is above the theoretical CpInf value for ThermoData object\n", - "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), thermo_coverage_dependence={}, comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", + "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", "The thermo for this species is probably wrong! Setting CpInf = Cphigh for Entropy calculationat T = 1666.6666666666665 K...\n", "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", @@ -620,7 +634,13 @@ "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", - "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Filling in rate rules in kinetics families by averaging...\n" ] }, @@ -1439,40 +1459,124 @@ "Initialization complete. Starting model generation.\n", "\n", "Generating initial reactions...\n", - "For reaction generation 1 process is used.\n", - "For reaction O2(3) + CH4(2) <=> HO2(50) + CH3(10), Ea raised from 216.4 to 231.5 kJ/mol to match endothermicity of reaction.\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction O2(3) + CH4(2) <=> HO2(50) + CH3(10), Ea raised from 216.4 to 231.5 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction O2(3) + CO2(4) <=> [O]OO[C]=O(82), Ea raised from 413.6 to 418.2 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CO2(4) <=> [O]OC([O])=O(83), Ea raised from 298.1 to 300.3 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + C2H6(8) <=> HO2(50) + C2H5(13), Ea raised from 207.3 to 215.7 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CH2O(9) <=> HO2(50) + HCO(15), Ea raised from 158.7 to 162.5 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CH2O(9) <=> [CH2]OO[O](84), Ea raised from 358.5 to 363.2 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CH2O(9) <=> [O]CO[O](85), Ea raised from 172.4 to 176.3 kJ/mol to match endothermicity of reaction.\n", - "For reaction O2(3) + C3H8(11) <=> HO2(50) + C[CH]C(53), Ea raised from 201.7 to 205.6 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C3H8(11) <=> HO2(50) + C[CH]C(53), Ea raised from 201.7 to 205.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction O2(3) + CH3OH(14) <=> HO2(50) + CH2OH(47), Ea raised from 178.9 to 195.8 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CH3CHO(16) <=> HO2(50) + C[C]=O(59), Ea raised from 157.1 to 167.1 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CH3CHO(16) <=> C[CH]OO[O](88), Ea raised from 373.4 to 376.8 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + CH3CHO(16) <=> CC([O])O[O](89), Ea raised from 176.0 to 178.9 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + OH(17) <=> O=O(90) + OH(17), Ea raised from 94.3 to 94.3 kJ/mol to match endothermicity of reaction.\n", "For reaction O2(3) + C2H4(18) <=> HO2(50) + [CH]=C(60), Ea raised from 251.1 to 256.1 kJ/mol to match endothermicity of reaction.\n", - "For reaction O2(3) + C2H4(18) <=> [CH2]CO[O](91), Ea raised from 132.2 to 156.2 kJ/mol to match endothermicity of reaction.\n", - "For reaction O2(3) + CH3OO(20) <=> COOO[O](93), Ea raised from 77.5 to 83.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H4(18) <=> [CH2]CO[O](91), Ea raised from 132.2 to 156.2 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction O2(3) + CH3OO(20) <=> COOO[O](93), Ea raised from 77.5 to 83.1 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction CO2(4) + CO2(4) <=> O=C1OOC1=O(97), Ea raised from 333.9 to 341.4 kJ/mol to match endothermicity of reaction.\n", "For reaction CO2(4) + CH2O(9) <=> O=C1COO1(106), Ea raised from 264.6 to 271.9 kJ/mol to match endothermicity of reaction.\n", "For reaction CO2(4) + CH3(10) <=> CC([O])=O(108), Ea raised from 58.1 to 65.1 kJ/mol to match endothermicity of reaction.\n", - "For reaction H(12) + CO2(4) <=> HCOO(38), Ea raised from 47.9 to 52.0 kJ/mol to match endothermicity of reaction.\n", + "For reaction H(12) + CO2(4) <=> HCOO(38), Ea raised from 47.9 to 52.0 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction CO2(4) + HCO(15) <=> [O]C(=O)C=O(122), Ea raised from 146.3 to 150.3 kJ/mol to match endothermicity of reaction.\n", - "For reaction CO2(4) + CH3CHO(16) <=> CC1OOC1=O(129), Ea raised from 271.9 to 279.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3CHO(16) <=> CC1OOC1=O(129), Ea raised from 271.9 to 279.8 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction CO2(4) + CH3CH(19) <=> C[CH]O[C]=O(141), Ea raised from 41.0 to 43.4 kJ/mol to match endothermicity of reaction.\n", - "For reaction CO2(4) + CH3OO(20) <=> COOO[C]=O(145), Ea raised from 251.4 to 255.6 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3OO(20) <=> COOO[C]=O(145), Ea raised from 251.4 to 255.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction CH2O(9) + CH2O(9) <=> C1COO1(169), Ea raised from 222.9 to 224.2 kJ/mol to match endothermicity of reaction.\n", - "For reaction CH2O(9) + CH3CHO(16) <=> CC1COO1(179), Ea raised from 243.0 to 245.6 kJ/mol to match endothermicity of reaction.\n", - "For reaction CH2O(9) + CH3OO(20) <=> [CH2]OOOC(188), Ea raised from 196.3 to 200.6 kJ/mol to match endothermicity of reaction.\n", - "For reaction COO(81) + [CH2]CC(54) <=> CH3OO(20) + C3H8(11) Ea raised from -7.3 to -7.3 kJ/mol.\n", - "For reaction CH3CHO(16) + CH3CHO(16) <=> CC1OOC1C(208), Ea raised from 263.7 to 267.1 kJ/mol to match endothermicity of reaction.\n", - "For reaction CH3OO(20) + CH3CHO(16) <=> C[CH]OOOC(218), Ea raised from 211.1 to 214.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction CH2O(9) + CH3CHO(16) <=> CC1COO1(179), Ea raised from 243.0 to 245.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH2O(9) + CH3OO(20) <=> [CH2]OOOC(188), Ea raised from 196.3 to 200.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction COO(81) + [CH2]CC(54) <=> CH3OO(20) + C3H8(11) Ea raised from -7.3 to -7.3 kJ/mol.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH3CHO(16) + CH3CHO(16) <=> CC1OOC1C(208), Ea raised from 263.7 to 267.1 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH3OO(20) + CH3CHO(16) <=> C[CH]OOOC(218), Ea raised from 211.1 to 214.2 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction HX(21) + CO2X(22) <=> X(1) + O=C[O][Pt](227), Ea raised from 83.9 to 85.5 kJ/mol to match endothermicity of reaction.\n", "For reaction OX(25) + H2OX(32) <=> HX(21) + O[O][Pt](246), Ea raised from 247.3 to 247.4 kJ/mol to match endothermicity of reaction.\n", - "Generating thermo for new species...\n", + "Generating thermo for new species...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Summary of Secondary Model Edge Enlargement\n", "---------------------------------\n", @@ -2338,11 +2442,23 @@ "Saving annotated version of Chemkin files...\n", "Chemkin file contains 545 reactions.\n", "Chemkin file contains 180 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Updating RMG execution statistics...\n", " Execution time (DD:HH:MM:SS): 00:00:00:39\n", - " Memory used: 884.97 MB\n", + " Memory used: 885.35 MB\n", "\n", "Making seed mechanism...\n", "Beginning model generation stage 1...\n", @@ -2351,8 +2467,14 @@ "Conducting simulation of reaction system 1...\n", "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", "Reached max number of objects...preparing to terminate\n", - "At time 4.6109e-09 s, species O[CH]=[Pt](229) at rate ratio 0.10030650099699316 exceeded the minimum rate for moving to model core of 0.1\n", - "terminating simulation due to interrupt...\n", + "At time 1.1717e-09 s, species O[CH]=[Pt](229) at rate ratio 0.10055149581982531 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "\n", "Adding species O[CH]=[Pt](229) to model core\n", @@ -2379,7 +2501,13 @@ " The model edge has 218 species and 605 reactions\n", "\n", "\n", - "For reaction generation 1 process is used.\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "For reaction OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229), Ea raised from 48.7 to 53.2 kJ/mol to match endothermicity of reaction.\n", "For reaction H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229), Ea raised from 143.8 to 146.0 kJ/mol to match endothermicity of reaction.\n", "For reaction X(1) + O[CH](O)[Pt](260) <=> OHX(31) + O[CH]=[Pt](229), Ea raised from 58.3 to 62.4 kJ/mol to match endothermicity of reaction.\n", @@ -2468,17 +2596,36 @@ "Saving annotated version of Chemkin files...\n", "Chemkin file contains 545 reactions.\n", "Chemkin file contains 228 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Updating RMG execution statistics...\n", - " Execution time (DD:HH:MM:SS): 00:00:00:50\n", - " Memory used: 886.73 MB\n", + " Execution time (DD:HH:MM:SS): 00:00:00:41\n", + " Memory used: 886.86 MB\n", "\n", - "Conducting simulation of reaction system 2...\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", "Reached max number of objects...preparing to terminate\n", - "At time 2.3883e-09 s, species O[C]#[Pt](228) at rate ratio 0.10006247522197795 exceeded the minimum rate for moving to model core of 0.1\n", - "terminating simulation due to interrupt...\n", + "At time 7.1525e-10 s, species O[C]#[Pt](228) at rate ratio 0.10126443245267676 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "\n", "Adding species O[C]#[Pt](228) to model core\n", @@ -2517,7 +2664,13 @@ " The model edge has 228 species and 633 reactions\n", "\n", "\n", - "For reaction generation 1 process is used.\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Generating thermo for new species...\n", "\n", "Summary of Secondary Model Edge Enlargement\n", @@ -2571,17 +2724,35 @@ "Saving annotated version of Chemkin files...\n", "Chemkin file contains 545 reactions.\n", "Chemkin file contains 249 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Updating RMG execution statistics...\n", - " Execution time (DD:HH:MM:SS): 00:00:01:03\n", - " Memory used: 886.74 MB\n", + " Execution time (DD:HH:MM:SS): 00:00:00:42\n", + " Memory used: 886.86 MB\n", "\n", "Making seed mechanism...\n", "Conducting simulation of reaction system 1...\n", - "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Reached max number of objects...preparing to terminate\n", - "At time 7.4162e-09 s, species COOH_X(37) at rate ratio 0.1003670787675018 exceeded the minimum rate for moving to model core of 0.1\n", + "At time 4.0484e-09 s, species COOH_X(37) at rate ratio 0.10000707758973901 exceeded the minimum rate for moving to model core of 0.1\n", "terminating simulation due to interrupt...\n", "\n", "\n", @@ -2612,12 +2783,2971 @@ " The model edge has 233 species and 643 reactions\n", "\n", "\n", - "For reaction generation 1 process is used.\n", - "Generating thermo for new species...\n", - "\n", - "Summary of Secondary Model Edge Enlargement\n", - "---------------------------------\n", - "Added 0 new core species\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 16 new edge species\n", + " O=C(O)C(=O)[O][Pt](270)\n", + " O=C(O)O[C](=O)[Pt](271)\n", + " O=C(O)[C](=O)[Pt](272)\n", + " O=C(O)[CH2][Pt](273)\n", + " CC(=O)O.[Pt](274)\n", + " O=C(O)[CH]=[Pt](275)\n", + " O=C(O)[C]#[Pt](276)\n", + " O=C(O)O(277)\n", + " OO[C](O)=[Pt](278)\n", + " O=C(O)O.[Pt](279)\n", + " O=CC(=O)O(280)\n", + " O=CC(=O)O.[Pt](281)\n", + " O=C(O)[CH](O)[Pt](282)\n", + " O=C(O)[C](O)=[Pt](283)\n", + " O=C(O)C(=O)O(284)\n", + " O=C(O)C(=O)O.[Pt](285)\n", + "Added 0 new core reactions\n", + "Created 55 new edge reactions\n", + " X(1) + X(1) + O=CO(98) <=> HX(21) + COOH_X(37)\n", + " X(1) + O[C](O)=[Pt](267) <=> HX(21) + COOH_X(37)\n", + " HX(21) + COOH_X(37) <=> X(1) + O=CO.[Pt](251)\n", + " COX(23) + O=C(O)[O][Pt](232) <=> CO2X(22) + COOH_X(37)\n", + " COX(23) + O=[C]([Pt])OO(233) <=> CO2X(22) + COOH_X(37)\n", + " X(1) + O=C(O)C(=O)[O][Pt](270) <=> CO2X(22) + COOH_X(37)\n", + " X(1) + O=C(O)O[C](=O)[Pt](271) <=> CO2X(22) + COOH_X(37)\n", + " COX(23) + OO[C]#[Pt](242) <=> COX(23) + COOH_X(37)\n", + " X(1) + O=C(O)[C](=O)[Pt](272) <=> COX(23) + COOH_X(37)\n", + " O=CO.[Pt](251) + CH3X(27) <=> COOH_X(37) + CH4X(24)\n", + " O[O][Pt](246) + COX(23) <=> OX(25) + COOH_X(37)\n", + " X(1) + O=C(O)[O][Pt](232) <=> OX(25) + COOH_X(37)\n", + " COOH_X(37) + CH2X(26) <=> COX(23) + CH2OH_X(48)\n", + " CHX(28) + O[C](O)=[Pt](267) <=> COOH_X(37) + CH2X(26)\n", + " CHX(28) + O=CO.[Pt](251) <=> COOH_X(37) + CH2X(26)\n", + " X(1) + O=C(O)[CH2][Pt](273) <=> COOH_X(37) + CH2X(26)\n", + " CH2X(26) + O[C](O)=[Pt](267) <=> COOH_X(37) + CH3X(27)\n", + " COX(23) + CH3OH_X(43) <=> COOH_X(37) + CH3X(27)\n", + " CH2X(26) + O=CO.[Pt](251) <=> COOH_X(37) + CH3X(27)\n", + " X(1) + X(1) + CC(=O)O(77) <=> COOH_X(37) + CH3X(27)\n", + " X(1) + CC(=O)O.[Pt](274) <=> COOH_X(37) + CH3X(27)\n", + " CX(29) + O[C](O)=[Pt](267) <=> CHX(28) + COOH_X(37)\n", + " CX(29) + O=CO.[Pt](251) <=> CHX(28) + COOH_X(37)\n", + " X(1) + O=C(O)[CH]=[Pt](275) <=> CHX(28) + COOH_X(37)\n", + " X(1) + O=C(O)[C]#[Pt](276) <=> CX(29) + COOH_X(37)\n", + " OX(25) + O[C](O)=[Pt](267) <=> OHX(31) + COOH_X(37)\n", + " OO.[Pt](259) + COX(23) <=> OHX(31) + COOH_X(37)\n", + " OX(25) + O=CO.[Pt](251) <=> OHX(31) + COOH_X(37)\n", + " X(1) + X(1) + O=C(O)O(277) <=> OHX(31) + COOH_X(37)\n", + " X(1) + OO[C](O)=[Pt](278) <=> OHX(31) + COOH_X(37)\n", + " X(1) + O=C(O)O.[Pt](279) <=> OHX(31) + COOH_X(37)\n", + " OHX(31) + O=CO.[Pt](251) <=> H2OX(32) + COOH_X(37)\n", + " COX(23) + OO[CH]=[Pt](250) <=> CHOX(33) + COOH_X(37)\n", + " COX(23) + O[C](O)=[Pt](267) <=> CHOX(33) + COOH_X(37)\n", + " CHOX(33) + COOH_X(37) <=> CO2X(22) + CH2O_X(46)\n", + " COX(23) + O=CO.[Pt](251) <=> CHOX(33) + COOH_X(37)\n", + " COX(23) + O=CO.[Pt](251) <=> CHOX(33) + COOH_X(37)\n", + " X(1) + X(1) + O=CC(=O)O(280) <=> CHOX(33) + COOH_X(37)\n", + " X(1) + O=CC(=O)O.[Pt](281) <=> CHOX(33) + COOH_X(37)\n", + " COOH_X(37) + O[CH]=[Pt](229) <=> COX(23) + O[CH](O)[Pt](260)\n", + " CHX(28) + OO[C](O)=[Pt](278) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[C](O)=[Pt](267) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " COOH_X(37) + O[CH]=[Pt](229) <=> CO2X(22) + CH2OH_X(48)\n", + " CHX(28) + O=C(O)O.[Pt](279) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O=CO.[Pt](251) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " X(1) + O=C(O)[CH](O)[Pt](282) <=> COOH_X(37) + O[CH]=[Pt](229)\n", + " COX(23) + O[C](O)=[Pt](267) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " CX(29) + OO[C](O)=[Pt](278) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " CX(29) + O=C(O)O.[Pt](279) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " X(1) + O=C(O)[C](O)=[Pt](283) <=> O[C]#[Pt](228) + COOH_X(37)\n", + " COX(23) + OO[C](O)=[Pt](278) <=> COOH_X(37) + COOH_X(37)\n", + " COOH_X(37) + COOH_X(37) <=> CO2X(22) + O=CO.[Pt](251)\n", + " COX(23) + O=C(O)O.[Pt](279) <=> COOH_X(37) + COOH_X(37)\n", + " X(1) + X(1) + O=C(O)C(=O)O(284) <=> COOH_X(37) + COOH_X(37)\n", + " X(1) + O=C(O)C(=O)O.[Pt](285) <=> COOH_X(37) + COOH_X(37)\n", + "\n", + "After model enlargement:\n", + " The model core has 39 species and 148 reactions\n", + " The model edge has 249 species and 698 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:45\n", + " Memory used: 886.86 MB\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "At time 6.5290e-06 s, reached target termination RateRatio: 0.04995584810912576\n", + " CH4(2) conversion: 0.7287 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "For reaction generation 1 process is used.\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 39 species and 148 reactions\n", + " The model edge has 249 species and 698 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:46\n", + " Memory used: 889.65 MB\n", + "\n", + "Making seed mechanism...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Performing final model checks...\n", + "No collision rate violators found in the model's core.\n", + "\n", + "MODEL GENERATION COMPLETED\n", + "\n", + "The final model core has 39 species and 148 reactions\n", + "The final model edge has 249 species and 698 reactions\n", + "\n", + "RMG execution terminated at Fri Apr 10 09:33:31 2026\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running RMG with input file at temp/cov_dep_rmg_run/input.py...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":root:Removing old /home/moon/rmg/RMG-Py/ipython/temp/cov_dep_rmg_run/RMG_backup.log\n", + ":root:Moving /home/moon/rmg/RMG-Py/ipython/temp/cov_dep_rmg_run/RMG.log to /home/moon/rmg/RMG-Py/ipython/temp/cov_dep_rmg_run/RMG_backup.log\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Global RMG Settings:\n", + " database.directory = /home/moon/rmg/RMG-database/input (Default, relative to RMG-Py source code)\n", + " test_data.directory = /home/moon/rmg/RMG-Py/test/rmgpy/test_data (Default, relative to RMG-Py source code)\n", + "RMG execution initiated at Fri Apr 10 09:33:35 2026\n", + "\n", + "#########################################################\n", + "# RMG-Py - Reaction Mechanism Generator in Python #\n", + "# Version: 3.3.0 #\n", + "# Authors: RMG Developers (rmg_dev@mit.edu) #\n", + "# P.I.s: William H. Green (whgreen@mit.edu) #\n", + "# Richard H. West (r.west@neu.edu) #\n", + "# Website: http://reactionmechanismgenerator.github.io/ #\n", + "#########################################################\n", + "\n", + "The current git HEAD for RMG-Py is:\n", + "\tb'd376fd6fe0f4c0cea57bf879cc4c2745afbeb52d'\n", + "\tb'Fri Apr 10 08:01:23 2026 -0400'\n", + "\n", + "The current git HEAD for RMG-database is:\n", + "\tb'edfb0e5d715146fd0edd2201a0749217781af2b3'\n", + "\tb'Tue Apr 7 16:09:33 2026 -0400'\n", + "\n", + "Reading input file \"/home/moon/rmg/RMG-Py/ipython/temp/cov_dep_rmg_run/input.py\"...\n", + "\n", + "# Data sources\n", + "database(\n", + " thermoLibraries=['surfaceThermoCovDepPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", + " reactionLibraries = [\n", + " ('Surface/Methane/Deutschmann_Pt', False),\n", + " ('Surface/Methane/Vlachos_Pt111', False),\n", + " 'BurkeH2O2inArHe'],\n", + " seedMechanisms = [],\n", + " kineticsDepositories = ['training'],\n", + " kineticsFamilies =['surface','default'],\n", + " kineticsEstimator = 'rate rules',\n", + ")\n", + "\n", + "catalystProperties( # default values for Pt(111)\n", + " metal = 'Pt111',\n", + " thermoCoverageDependence = True,\n", + ")\n", + "\n", + "# List of species\n", + "species(\n", + " label='X',\n", + " reactive=True,\n", + " structure=adjacencyList(\"1 X u0\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH4',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH4]\"),\n", + ")\n", + "species(\n", + " label='O2',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u1 p2 c0 {2,S}\n", + " 2 O u1 p2 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='Ar',\n", + " reactive=False,\n", + " structure=SMILES(\"[Ar]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CO2',\n", + " reactive=True,\n", + " structure=SMILES(\"O=C=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='H2O',\n", + " reactive=True,\n", + " structure=SMILES(\"O\"),\n", + ")\n", + "\n", + "species(\n", + " label='H2',\n", + " reactive=True,\n", + " structure=SMILES(\"[H][H]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CO',\n", + " reactive=True,\n", + " structure=SMILES(\"[C-]#[O+]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H6',\n", + " reactive=True,\n", + " structure=SMILES(\"CC\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH2O',\n", + " reactive=True,\n", + " structure=SMILES(\"C=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH3]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C3H8',\n", + " reactive=True,\n", + " structure=SMILES(\"CCC\"),\n", + ")\n", + "\n", + "species(\n", + " label='H',\n", + " reactive=True,\n", + " structure=SMILES(\"[H]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H5',\n", + " reactive=True,\n", + " structure=SMILES(\"C[CH2]\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3OH',\n", + " reactive=True,\n", + " structure=SMILES(\"CO\"),\n", + ")\n", + "\n", + "species(\n", + " label='HCO',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH]=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3CHO',\n", + " reactive=True,\n", + " structure=SMILES(\"CC=O\"),\n", + ")\n", + "\n", + "species(\n", + " label='OH',\n", + " reactive=True,\n", + " structure=SMILES(\"[OH]\"),\n", + ")\n", + "\n", + "species(\n", + " label='C2H4',\n", + " reactive=True,\n", + " structure=SMILES(\"C=C\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3CH',\n", + " reactive=True,\n", + " structure=SMILES(\"[CH]C\"),\n", + ")\n", + "\n", + "species(\n", + " label='CH3OO',\n", + " reactive=True,\n", + " structure=SMILES(\"CO[O]\"),\n", + ")\n", + "\n", + "species(\n", + " label='HX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 H u0 p0 c0 {2,S}\n", + " 2 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CO2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {3,D}\n", + " 2 O u0 p2 c0 {3,D}\n", + " 3 C u0 p0 c0 {1,D} {2,D}\n", + " 4 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='COX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 C u0 p0 c0 {1,D} {3,D}\n", + " 3 X u0 p0 c0 {2,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH4X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 H u0 p0 c0 {1,S}\n", + " 5 H u0 p0 c0 {1,S}\n", + " 6 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='OX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 X u0 p0 c0 {1,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,D}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 X u0 p0 c0 {1,D}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CH3X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 H u0 p0 c0 {1,S}\n", + " 5 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CHX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,S} {3,T}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0 {1,T}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 C u0 p0 c0 {2,Q}\n", + " 2 X u0 p0 c0 {1,Q}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='H2X',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 H u0 p0 c0 {2,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='OHX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,S} {3,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 X u0 p0 c0 {1,S}\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='H2OX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,S} {3,S}\n", + " 2 H u0 p0 c0 {1,S}\n", + " 3 H u0 p0 c0 {1,S}\n", + " 4 X u0 p0 c0\n", + " '''),\n", + ")\n", + "\n", + "species(\n", + " label='CHOX',\n", + " reactive=True,\n", + " structure=adjacencyList(\n", + " '''\n", + " 1 O u0 p2 c0 {2,D}\n", + " 2 C u0 p0 c0 {1,D} {3,S} {4,S}\n", + " 3 H u0 p0 c0 {2,S}\n", + " 4 X u0 p0 c0 {2,S}\n", + " '''),\n", + ")\n", + "\n", + "surfaceReactor(\n", + " temperature=(1000,'K'),\n", + " initialPressure=(1.0, 'bar'),\n", + " initialGasMoleFractions={\n", + " \"CH4\": 0.041866,\n", + " \"O2\": 0.03488,\n", + " \"Ar\": 0.131246,\n", + " },\n", + " initialSurfaceCoverages={\n", + " \"X\": 1.0,\n", + " },\n", + " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", + " terminationConversion = { \"CH4\":0.95,},\n", + " terminationTime=(10., 's'),\n", + " # terminationConversion={'O2': 0.99,},\n", + " terminationRateRatio=0.05\n", + ")\n", + "\n", + "\n", + "simulator(\n", + " atol=1e-18,\n", + " rtol=1e-12,\n", + ")\n", + "\n", + "model(\n", + " toleranceKeepInEdge=0.0,\n", + " toleranceMoveToCore=1e-1,\n", + " toleranceInterruptSimulation=1e8,\n", + " maximumEdgeSpecies=500000,\n", + " maxNumSpecies=40,\n", + ")\n", + "\n", + "options(\n", + " units='si',\n", + " saveRestartPeriod=None,\n", + " generateOutputHTML=True,\n", + " generatePlots=False,\n", + " saveEdgeSpecies=True,\n", + " saveSimulationProfiles=True,\n", + ")\n", + "\n", + "generatedSpeciesConstraints(\n", + " allowed=['input species','reaction libraries'],\n", + " maximumSurfaceSites=1,\n", + ")\n", + "\n", + "Using catalyst surface properties from metal 'Pt111'.\n", + "Using binding energies:\n", + "{'H': (-2.75368,'eV/molecule'), 'C': (-7.02516,'eV/molecule'), 'N': (-4.63225,'eV/molecule'), 'O': (-3.81153,'eV/molecule')}\n", + "Using surface site density: (2.483e-09,'mol/cm^2')\n", + "Coverage dependence is turned OFF\n", + "Warning: Initial gas mole fractions do not sum to one; renormalizing.\n", + "\n", + "\n", + "Surface reaction system 1\n", + "Gas phase mole fractions:\n", + " CH4 0.20129\n", + " O2 0.1677\n", + " Ar 0.63101\n", + "Total gas phase: 1 moles\n", + "Pressure: 1e+05 Pa\n", + "Temperature: 1000.0 K\n", + "Reactor volume: 0.0831 m3\n", + "Surface/volume ratio: 1e+05 m2/m3\n", + "Surface site density: 2.48e-05 mol/m2\n", + "Surface sites in reactor: 0.206 moles\n", + "Initial surface coverages (and amounts):\n", + " X 1 = 0.20645 moles\n", + "Warning: Generate Output HTML option was turned on. Note that this will slow down model generation.\n", + "Warning: Edge species saving was turned on. This will slow down model generation for large simulations.\n", + "\n", + "Loading transport library from PrimaryTransportLibrary.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from OneDMinN2.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from NOx2018.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading transport library from GRI-Mech.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport library from NIST_Fluorine.py in /home/moon/rmg/RMG-database/input/transport/libraries...\n", + "Loading transport group database from /home/moon/rmg/RMG-database/input/transport/groups...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading kinetics library Surface/Methane/Deutschmann_Pt from /home/moon/rmg/RMG-database/input/kinetics/libraries/Surface/Methane/Deutschmann_Pt/reactions.py...\n", + "Loading kinetics library Surface/Methane/Vlachos_Pt111 from /home/moon/rmg/RMG-database/input/kinetics/libraries/Surface/Methane/Vlachos_Pt111/reactions.py...\n", + "Loading kinetics library BurkeH2O2inArHe from /home/moon/rmg/RMG-database/input/kinetics/libraries/BurkeH2O2inArHe/reactions.py...\n", + "Loading frequencies group database from /home/moon/rmg/RMG-database/input/statmech/groups...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading solvation thermodynamics group database from /home/moon/rmg/RMG-database/input/solvation/groups...\n", + "Loading thermodynamics library from surfaceThermoCovDepPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from surfaceThermoPt111.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from primaryThermoLibrary.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics library from thermo_DFT_CCSDTF12_BAC.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading thermodynamics library from DFT_QCI_thermo.py in /home/moon/rmg/RMG-database/input/thermo/libraries...\n", + "Loading thermodynamics group database from /home/moon/rmg/RMG-database/input/thermo/groups...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trimolecular reactions are turned on\n", + "Adding rate rules from training set in kinetics families...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Cphigh is above the theoretical CpInf value for ThermoData object\n", + "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", + "The thermo for this species is probably wrong! Setting CpInf = Cphigh for Entropy calculationat T = 2000.0 K...\n", + "Warning: Cphigh is above the theoretical CpInf value for ThermoData object\n", + "ThermoData(Tdata=([300,400,500,600,800,1000,1500],'K'), Cpdata=([60.2599,68.0494,74.7775,80.9311,90.8846,97.4337,105.393],'J/(mol*K)'), H298=(-477.191,'kJ/mol'), S298=(269.551,'J/(mol*K)'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(103.931,'J/(mol*K)'), comment=\"\"\"Thermo group additivity estimation: group(O2s-(Cds-Cd)(Cds-Cd)) + group(O2s-(Cds-O2d)H) + group(Cds-OdOsOs) + group(Li-OCOdO) + radical(OC=OOJ)\"\"\").\n", + "The thermo for this species is probably wrong! Setting CpInf = Cphigh for Entropy calculationat T = 1666.6666666666665 K...\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Ni'. Using 'Ni111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Pt'. Using 'Pt111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n", + "Warning: Found multiple binding energies for 'Cu'. Using 'Cu111'.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filling in rate rules in kinetics families by averaging...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=9.78829e-18): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=8.30526e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.38146e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=8.43874e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=2.11249e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=5.2787e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=2.26538e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.9079e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=7.53573e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=7.43345e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.47964e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.34061e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=8.87325e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.20362e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=2.39961e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.56405e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=4.40143e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=4.71804e-22): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=4.97734e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=7.47076e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=6.10745e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.69387e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.30536e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=1.51603e-20): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/miniforge3/envs/rmg_env/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2322: LinAlgWarning: Ill-conditioned matrix (rcond=3.18757e-21): result may not be accurate.\n", + " fu = func(x, *args)\n", + "/home/moon/rmg/RMG-Py/rmgpy/thermo/thermoengine.py:100: LinAlgWarning: Ill-conditioned matrix (rcond=3.18757e-21): result may not be accurate.\n", + " thermo = wilhoit.to_nasa(Tmin=100.0, Tmax=5000.0, Tint=1000.0)\n", + "/home/moon/rmg/RMG-Py/rmgpy/thermo/thermoengine.py:100: LinAlgWarning: Ill-conditioned matrix (rcond=1.08953e-21): result may not be accurate.\n", + " thermo = wilhoit.to_nasa(Tmin=100.0, Tmax=5000.0, Tint=1000.0)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding reaction library Surface/Methane/Deutschmann_Pt to model edge...\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 18 new edge reactions\n", + " X(1) + X(1) + H2(6) <=> HX(21) + HX(21)\n", + " X(1) + X(1) + O2(3) <=> OX(25) + OX(25)\n", + " X(1) + X(1) + CH4(2) <=> HX(21) + CH3X(27)\n", + " X(1) + OX(25) + CH4(2) <=> OHX(31) + CH3X(27)\n", + " X(1) + OHX(31) + CH4(2) <=> H2OX(32) + CH3X(27)\n", + " X(1) + H2O(5) <=> H2OX(32)\n", + " X(1) + CO2(4) <=> CO2X(22)\n", + " X(1) + CO(7) <=> COX(23)\n", + " OX(25) + CX(29) <=> X(1) + COX(23)\n", + " OX(25) + COX(23) <=> X(1) + CO2X(22)\n", + " HX(21) + CO2X(22) <=> OHX(31) + COX(23)\n", + " HX(21) + CH2X(26) <=> X(1) + CH3X(27)\n", + " HX(21) + CHX(28) <=> X(1) + CH2X(26)\n", + " X(1) + CHX(28) <=> HX(21) + CX(29)\n", + " OX(25) + HX(21) <=> X(1) + OHX(31)\n", + " OX(25) + H2OX(32) <=> OHX(31) + OHX(31)\n", + " H2(6) + CX(29) <=> CH2X(26)\n", + " HX(21) + OHX(31) <=> X(1) + H2OX(32)\n", + "\n", + "After model enlargement:\n", + " The model core has 0 species and 0 reactions\n", + " The model edge has 34 species and 18 reactions\n", + "\n", + "Adding reaction library Surface/Methane/Vlachos_Pt111 to model edge...\n", + "This library reaction was not new: X(1) + X(1) + O2(3) <=> OX(25) + OX(25)\n", + "This library reaction was not new: X(1) + CO2(4) <=> CO2X(22)\n", + "This library reaction was not new: X(1) + CO2X(22) <=> OX(25) + COX(23)\n", + "This library reaction was not new: X(1) + X(1) + H2(6) <=> HX(21) + HX(21)\n", + "This library reaction was not new: X(1) + OHX(31) <=> OX(25) + HX(21)\n", + "This library reaction was not new: X(1) + H2OX(32) <=> HX(21) + OHX(31)\n", + "This library reaction was not new: OX(25) + H2OX(32) <=> OHX(31) + OHX(31)\n", + "This library reaction was not new: X(1) + H2O(5) <=> H2OX(32)\n", + "This library reaction was not new: HX(21) + CO2X(22) <=> OHX(31) + COX(23)\n", + "This library reaction was not new: X(1) + X(1) + CH4(2) <=> HX(21) + CH3X(27)\n", + "This library reaction was not new: X(1) + CH3X(27) <=> HX(21) + CH2X(26)\n", + "This library reaction was not new: X(1) + CH2X(26) <=> HX(21) + CHX(28)\n", + "This library reaction was not new: X(1) + CHX(28) <=> HX(21) + CX(29)\n", + "This library reaction was not new: X(1) + COX(23) <=> OX(25) + CX(29)\n", + "Warning: Species CO from reaction library Surface/Methane/Vlachos_Pt111 is globally forbidden. It will behave as an inert unless found in a seed mechanism or reaction library.\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 15 new edge species\n", + " O(34)\n", + " CO(35)\n", + " COOH(36)\n", + " COOH_X(37)\n", + " HCOO(38)\n", + " HCOO_XX(39)\n", + " C(40)\n", + " CH(41)\n", + " CH2(42)\n", + " CH3OH_X(43)\n", + " CH3O(44)\n", + " CH3O_X(45)\n", + " CH2O_X(46)\n", + " CH2OH(47)\n", + " CH2OH_X(48)\n", + "Added 0 new core reactions\n", + "Created 37 new edge reactions\n", + " X(1) + O(34) <=> OX(25)\n", + " X(1) + CO(35) <=> COX(23)\n", + " X(1) + OH(17) <=> OHX(31)\n", + " X(1) + H(12) <=> HX(21)\n", + " X(1) + COOH(36) <=> COOH_X(37)\n", + " X(1) + COOH_X(37) <=> OHX(31) + COX(23)\n", + " X(1) + COOH_X(37) <=> HX(21) + CO2X(22)\n", + " H2OX(32) + COX(23) <=> HX(21) + COOH_X(37)\n", + " OHX(31) + CO2X(22) <=> OX(25) + COOH_X(37)\n", + " H2OX(32) + CO2X(22) <=> OHX(31) + COOH_X(37)\n", + " X(1) + X(1) + HCOO(38) <=> HCOO_XX(39)\n", + " HX(21) + CO2X(22) <=> HCOO_XX(39)\n", + " X(1) + OHX(31) + CO2X(22) <=> OX(25) + HCOO_XX(39)\n", + " X(1) + C(40) <=> CX(29)\n", + " X(1) + CH(41) <=> CHX(28)\n", + " X(1) + CH2(42) <=> CH2X(26)\n", + " X(1) + CH3(10) <=> CH3X(27)\n", + " OX(25) + CH3X(27) <=> OHX(31) + CH2X(26)\n", + " OHX(31) + CHX(28) <=> OX(25) + CH2X(26)\n", + " OHX(31) + CX(29) <=> OX(25) + CHX(28)\n", + " H2OX(32) + CH2X(26) <=> OHX(31) + CH3X(27)\n", + " H2OX(32) + CHX(28) <=> OHX(31) + CH2X(26)\n", + " H2OX(32) + CX(29) <=> OHX(31) + CHX(28)\n", + " HX(21) + COX(23) <=> OX(25) + CHX(28)\n", + " HX(21) + COX(23) <=> OHX(31) + CX(29)\n", + " COX(23) + COX(23) <=> CX(29) + CO2X(22)\n", + " X(1) + CH3OH(14) <=> CH3OH_X(43)\n", + " X(1) + CH3O(44) <=> CH3O_X(45)\n", + " X(1) + CH2O(9) <=> CH2O_X(46)\n", + " X(1) + HCO(15) <=> CHOX(33)\n", + " X(1) + CH2OH(47) <=> CH2OH_X(48)\n", + " X(1) + CH3OH_X(43) <=> HX(21) + CH3O_X(45)\n", + " X(1) + CH3O_X(45) <=> HX(21) + CH2O_X(46)\n", + " X(1) + CH2O_X(46) <=> HX(21) + CHOX(33)\n", + " X(1) + CHOX(33) <=> HX(21) + COX(23)\n", + " X(1) + CH3OH_X(43) <=> HX(21) + CH2OH_X(48)\n", + " X(1) + CH2OH_X(48) <=> HX(21) + CH2O_X(46)\n", + "\n", + "After model enlargement:\n", + " The model core has 0 species and 0 reactions\n", + " The model edge has 49 species and 55 reactions\n", + "\n", + "Adding reaction library BurkeH2O2inArHe to model edge...\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 3 new edge species\n", + " He(49)\n", + " HO2(50)\n", + " H2O2(51)\n", + "Added 0 new core reactions\n", + "Created 30 new edge reactions\n", + " O2(3) + H(12) <=> O(34) + OH(17)\n", + " O(34) + H2(6) <=> H(12) + OH(17)\n", + " OH(17) + H2(6) <=> H(12) + H2O(5)\n", + " OH(17) + OH(17) <=> O(34) + H2O(5)\n", + " H2(6) <=> H(12) + H(12)\n", + " Ar + H2(6) <=> Ar + H(12) + H(12)\n", + " He(49) + H2(6) <=> He(49) + H(12) + H(12)\n", + " O(34) + O(34) <=> O2(3)\n", + " Ar + O(34) + O(34) <=> Ar + O2(3)\n", + " He(49) + O(34) + O(34) <=> He(49) + O2(3)\n", + " O(34) + H(12) <=> OH(17)\n", + " H2O(5) <=> H(12) + OH(17)\n", + " H2O(5) + H2O(5) <=> H(12) + OH(17) + H2O(5)\n", + " O2(3) + H(12) <=> HO2(50)\n", + " H(12) + HO2(50) <=> O2(3) + H2(6)\n", + " H(12) + HO2(50) <=> OH(17) + OH(17)\n", + " O(34) + HO2(50) <=> O2(3) + OH(17)\n", + " OH(17) + HO2(50) <=> O2(3) + H2O(5)\n", + " HO2(50) + HO2(50) <=> O2(3) + H2O2(51)\n", + " H2O2(51) <=> OH(17) + OH(17)\n", + " H(12) + H2O2(51) <=> OH(17) + H2O(5)\n", + " H(12) + H2O2(51) <=> HO2(50) + H2(6)\n", + " O(34) + H2O2(51) <=> OH(17) + HO2(50)\n", + " OH(17) + H2O2(51) <=> HO2(50) + H2O(5)\n", + " H(12) + HO2(50) <=> O(34) + H2O(5)\n", + " H(12) + HO2(50) <=> H2O2(51)\n", + " OH(17) + OH(17) <=> O2(3) + H2(6)\n", + " O(34) + H2O(5) <=> O2(3) + H2(6)\n", + " O(34) + H2O2(51) <=> O2(3) + H2O(5)\n", + " O(34) + OH(17) <=> HO2(50)\n", + "\n", + "After model enlargement:\n", + " The model core has 0 species and 0 reactions\n", + " The model edge has 52 species and 85 reactions\n", + "\n", + "Warning: Input species CH3CH is globally forbidden but will be explicitly allowed since input species are permitted by the user's species constraints\n", + "NOT generating reactions for unreactive species Ar\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " Ar\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 1 species and 0 reactions\n", + " The model edge has 51 species and 85 reactions\n", + "\n", + "\n", + "NOT generating reactions for unreactive species Ne\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " Ne\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 2 species and 0 reactions\n", + " The model edge has 51 species and 85 reactions\n", + "\n", + "\n", + "NOT generating reactions for unreactive species N2\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " N2\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 3 species and 0 reactions\n", + " The model edge has 51 species and 85 reactions\n", + "\n", + "\n", + "Adding species X(1) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " X(1)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 4 species and 0 reactions\n", + " The model edge has 50 species and 85 reactions\n", + "\n", + "\n", + "Adding species CH4(2) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH4(2)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 5 species and 0 reactions\n", + " The model edge has 49 species and 85 reactions\n", + "\n", + "\n", + "Adding species O2(3) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " O2(3)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 6 species and 0 reactions\n", + " The model edge has 48 species and 85 reactions\n", + "\n", + "\n", + "Adding species CO2(4) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CO2(4)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 7 species and 0 reactions\n", + " The model edge has 47 species and 85 reactions\n", + "\n", + "\n", + "Adding species H2O(5) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2O(5)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 8 species and 0 reactions\n", + " The model edge has 46 species and 85 reactions\n", + "\n", + "\n", + "Adding species H2(6) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2(6)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 9 species and 0 reactions\n", + " The model edge has 45 species and 85 reactions\n", + "\n", + "\n", + "Adding species CO(7) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CO(7)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 10 species and 0 reactions\n", + " The model edge has 44 species and 85 reactions\n", + "\n", + "\n", + "Adding species C2H6(8) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C2H6(8)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 11 species and 0 reactions\n", + " The model edge has 43 species and 85 reactions\n", + "\n", + "\n", + "Adding species CH2O(9) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH2O(9)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 12 species and 0 reactions\n", + " The model edge has 42 species and 85 reactions\n", + "\n", + "\n", + "Adding species CH3(10) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3(10)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 13 species and 0 reactions\n", + " The model edge has 41 species and 85 reactions\n", + "\n", + "\n", + "Adding species C3H8(11) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C3H8(11)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 14 species and 0 reactions\n", + " The model edge has 40 species and 85 reactions\n", + "\n", + "\n", + "Adding species H(12) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H(12)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " H2(6) <=> H(12) + H(12)\n", + " Ar + H2(6) <=> Ar + H(12) + H(12)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 15 species and 2 reactions\n", + " The model edge has 39 species and 83 reactions\n", + "\n", + "\n", + "Adding species C2H5(13) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C2H5(13)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 16 species and 2 reactions\n", + " The model edge has 38 species and 83 reactions\n", + "\n", + "\n", + "Adding species CH3OH(14) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3OH(14)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 17 species and 2 reactions\n", + " The model edge has 37 species and 83 reactions\n", + "\n", + "\n", + "Adding species HCO(15) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " HCO(15)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 18 species and 2 reactions\n", + " The model edge has 36 species and 83 reactions\n", + "\n", + "\n", + "Adding species CH3CHO(16) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3CHO(16)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 19 species and 2 reactions\n", + " The model edge has 35 species and 83 reactions\n", + "\n", + "\n", + "Adding species OH(17) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " OH(17)\n", + "Created 0 new edge species\n", + "Moved 4 reactions from edge to core\n", + " OH(17) + H2(6) <=> H(12) + H2O(5)\n", + " H2O(5) <=> H(12) + OH(17)\n", + " H2O(5) + H2O(5) <=> H(12) + OH(17) + H2O(5)\n", + " OH(17) + OH(17) <=> O2(3) + H2(6)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 20 species and 6 reactions\n", + " The model edge has 34 species and 79 reactions\n", + "\n", + "\n", + "Adding species C2H4(18) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " C2H4(18)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 21 species and 6 reactions\n", + " The model edge has 33 species and 79 reactions\n", + "\n", + "\n", + "Adding species CH3CH(19) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3CH(19)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 22 species and 6 reactions\n", + " The model edge has 32 species and 79 reactions\n", + "\n", + "\n", + "Adding species CH3OO(20) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3OO(20)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 23 species and 6 reactions\n", + " The model edge has 31 species and 79 reactions\n", + "\n", + "\n", + "Adding species HX(21) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " HX(21)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " X(1) + X(1) + H2(6) <=> HX(21) + HX(21)\n", + " X(1) + H(12) <=> HX(21)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 24 species and 8 reactions\n", + " The model edge has 30 species and 77 reactions\n", + "\n", + "\n", + "Adding species CO2X(22) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CO2X(22)\n", + "Created 0 new edge species\n", + "Moved 1 reactions from edge to core\n", + " X(1) + CO2(4) <=> CO2X(22)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 25 species and 9 reactions\n", + " The model edge has 29 species and 76 reactions\n", + "\n", + "\n", + "Adding species COX(23) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " COX(23)\n", + "Created 0 new edge species\n", + "Moved 1 reactions from edge to core\n", + " X(1) + CO(7) <=> COX(23)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 26 species and 10 reactions\n", + " The model edge has 28 species and 75 reactions\n", + "\n", + "\n", + "Adding species CH4X(24) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH4X(24)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 27 species and 10 reactions\n", + " The model edge has 27 species and 75 reactions\n", + "\n", + "\n", + "Adding species OX(25) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " OX(25)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " X(1) + X(1) + O2(3) <=> OX(25) + OX(25)\n", + " OX(25) + COX(23) <=> X(1) + CO2X(22)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 28 species and 12 reactions\n", + " The model edge has 26 species and 73 reactions\n", + "\n", + "\n", + "Adding species CH2X(26) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH2X(26)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 29 species and 12 reactions\n", + " The model edge has 25 species and 73 reactions\n", + "\n", + "\n", + "Adding species CH3X(27) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CH3X(27)\n", + "Created 0 new edge species\n", + "Moved 3 reactions from edge to core\n", + " X(1) + X(1) + CH4(2) <=> HX(21) + CH3X(27)\n", + " HX(21) + CH2X(26) <=> X(1) + CH3X(27)\n", + " X(1) + CH3(10) <=> CH3X(27)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 30 species and 15 reactions\n", + " The model edge has 24 species and 70 reactions\n", + "\n", + "\n", + "Adding species CHX(28) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CHX(28)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " HX(21) + CHX(28) <=> X(1) + CH2X(26)\n", + " HX(21) + COX(23) <=> OX(25) + CHX(28)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 31 species and 17 reactions\n", + " The model edge has 23 species and 68 reactions\n", + "\n", + "\n", + "Adding species CX(29) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CX(29)\n", + "Created 0 new edge species\n", + "Moved 4 reactions from edge to core\n", + " OX(25) + CX(29) <=> X(1) + COX(23)\n", + " X(1) + CHX(28) <=> HX(21) + CX(29)\n", + " H2(6) + CX(29) <=> CH2X(26)\n", + " COX(23) + COX(23) <=> CX(29) + CO2X(22)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 32 species and 21 reactions\n", + " The model edge has 22 species and 64 reactions\n", + "\n", + "\n", + "Adding species H2X(30) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2X(30)\n", + "Created 0 new edge species\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 33 species and 21 reactions\n", + " The model edge has 21 species and 64 reactions\n", + "\n", + "\n", + "Adding species OHX(31) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " OHX(31)\n", + "Created 0 new edge species\n", + "Moved 8 reactions from edge to core\n", + " X(1) + OX(25) + CH4(2) <=> OHX(31) + CH3X(27)\n", + " HX(21) + CO2X(22) <=> OHX(31) + COX(23)\n", + " OX(25) + HX(21) <=> X(1) + OHX(31)\n", + " X(1) + OH(17) <=> OHX(31)\n", + " OX(25) + CH3X(27) <=> OHX(31) + CH2X(26)\n", + " OHX(31) + CHX(28) <=> OX(25) + CH2X(26)\n", + " OHX(31) + CX(29) <=> OX(25) + CHX(28)\n", + " HX(21) + COX(23) <=> OHX(31) + CX(29)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 34 species and 29 reactions\n", + " The model edge has 20 species and 56 reactions\n", + "\n", + "\n", + "Adding species H2OX(32) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " H2OX(32)\n", + "Created 0 new edge species\n", + "Moved 7 reactions from edge to core\n", + " X(1) + OHX(31) + CH4(2) <=> H2OX(32) + CH3X(27)\n", + " X(1) + H2O(5) <=> H2OX(32)\n", + " OX(25) + H2OX(32) <=> OHX(31) + OHX(31)\n", + " HX(21) + OHX(31) <=> X(1) + H2OX(32)\n", + " H2OX(32) + CH2X(26) <=> OHX(31) + CH3X(27)\n", + " H2OX(32) + CHX(28) <=> OHX(31) + CH2X(26)\n", + " H2OX(32) + CX(29) <=> OHX(31) + CHX(28)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 35 species and 36 reactions\n", + " The model edge has 19 species and 49 reactions\n", + "\n", + "\n", + "Adding species CHOX(33) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " CHOX(33)\n", + "Created 0 new edge species\n", + "Moved 2 reactions from edge to core\n", + " X(1) + HCO(15) <=> CHOX(33)\n", + " X(1) + CHOX(33) <=> HX(21) + COX(23)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 36 species and 38 reactions\n", + " The model edge has 18 species and 47 reactions\n", + "\n", + "\n", + "Initialization complete. Starting model generation.\n", + "\n", + "Generating initial reactions...\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction O2(3) + CH4(2) <=> HO2(50) + CH3(10), Ea raised from 216.4 to 231.5 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction O2(3) + CO2(4) <=> [O]OO[C]=O(82), Ea raised from 413.6 to 418.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CO2(4) <=> [O]OC([O])=O(83), Ea raised from 298.1 to 300.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H6(8) <=> HO2(50) + C2H5(13), Ea raised from 207.3 to 215.7 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH2O(9) <=> HO2(50) + HCO(15), Ea raised from 158.7 to 162.5 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH2O(9) <=> [CH2]OO[O](84), Ea raised from 358.5 to 363.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH2O(9) <=> [O]CO[O](85), Ea raised from 172.4 to 176.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C3H8(11) <=> HO2(50) + C[CH]C(53), Ea raised from 201.7 to 205.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction O2(3) + CH3OH(14) <=> HO2(50) + CH2OH(47), Ea raised from 178.9 to 195.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3CHO(16) <=> HO2(50) + C[C]=O(59), Ea raised from 157.1 to 167.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3CHO(16) <=> C[CH]OO[O](88), Ea raised from 373.4 to 376.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + CH3CHO(16) <=> CC([O])O[O](89), Ea raised from 176.0 to 178.9 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + OH(17) <=> O=O(90) + OH(17), Ea raised from 94.3 to 94.3 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H4(18) <=> HO2(50) + [CH]=C(60), Ea raised from 251.1 to 256.1 kJ/mol to match endothermicity of reaction.\n", + "For reaction O2(3) + C2H4(18) <=> [CH2]CO[O](91), Ea raised from 132.2 to 156.2 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction O2(3) + CH3OO(20) <=> COOO[O](93), Ea raised from 77.5 to 83.1 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CO2(4) + CO2(4) <=> O=C1OOC1=O(97), Ea raised from 333.9 to 341.4 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH2O(9) <=> O=C1COO1(106), Ea raised from 264.6 to 271.9 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3(10) <=> CC([O])=O(108), Ea raised from 58.1 to 65.1 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction H(12) + CO2(4) <=> HCOO(38), Ea raised from 47.9 to 52.0 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + HCO(15) <=> [O]C(=O)C=O(122), Ea raised from 146.3 to 150.3 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CO2(4) + CH3CHO(16) <=> CC1OOC1=O(129), Ea raised from 271.9 to 279.8 kJ/mol to match endothermicity of reaction.\n", + "For reaction CO2(4) + CH3CH(19) <=> C[CH]O[C]=O(141), Ea raised from 41.0 to 43.4 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CO2(4) + CH3OO(20) <=> COOO[C]=O(145), Ea raised from 251.4 to 255.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH2O(9) + CH2O(9) <=> C1COO1(169), Ea raised from 222.9 to 224.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction CH2O(9) + CH3CHO(16) <=> CC1COO1(179), Ea raised from 243.0 to 245.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH2O(9) + CH3OO(20) <=> [CH2]OOOC(188), Ea raised from 196.3 to 200.6 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction COO(81) + [CH2]CC(54) <=> CH3OO(20) + C3H8(11) Ea raised from -7.3 to -7.3 kJ/mol.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH3CHO(16) + CH3CHO(16) <=> CC1OOC1C(208), Ea raised from 263.7 to 267.1 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction CH3OO(20) + CH3CHO(16) <=> C[CH]OOOC(218), Ea raised from 211.1 to 214.2 kJ/mol to match endothermicity of reaction.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction HX(21) + CO2X(22) <=> X(1) + O=C[O][Pt](227), Ea raised from 83.9 to 85.5 kJ/mol to match endothermicity of reaction.\n", + "For reaction OX(25) + H2OX(32) <=> HX(21) + O[O][Pt](246), Ea raised from 247.3 to 247.4 kJ/mol to match endothermicity of reaction.\n", + "Generating thermo for new species...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 201 new edge species\n", + " [CH2](52)\n", + " C[CH]C(53)\n", + " [CH2]CC(54)\n", + " [CH2][CH2](55)\n", + " [CH2]C[O](56)\n", + " C=CO(57)\n", + " [CH2]C=O(58)\n", + " C[C]=O(59)\n", + " [CH]=C(60)\n", + " [CH]C(61)\n", + " [CH][CH2](62)\n", + " [CH2]O[O](63)\n", + " C[CH2][Pt](64)\n", + " CC.[Pt](65)\n", + " C[CH](C)[Pt](66)\n", + " CC[CH2][Pt](67)\n", + " CCC.[Pt](68)\n", + " O=C[CH2][Pt](69)\n", + " C[C](=O)[Pt](70)\n", + " C[CH]=[Pt](71)\n", + " CC=O.[Pt](72)\n", + " C=[CH][Pt](73)\n", + " C=C.[Pt](74)\n", + " CO[O][Pt](75)\n", + " COC=O(76)\n", + " CC(=O)O(77)\n", + " [CH]O(78)\n", + " C[CH]O(79)\n", + " CC[O](80)\n", + " COO(81)\n", + " [O]OO[C]=O(82)\n", + " [O]OC([O])=O(83)\n", + " [CH2]OO[O](84)\n", + " [O]CO[O](85)\n", + " CCO[O](86)\n", + " [O]OC=O(87)\n", + " C[CH]OO[O](88)\n", + " CC([O])O[O](89)\n", + " O=O(90)\n", + " [CH2]CO[O](91)\n", + " C[CH]O[O](92)\n", + " COOO[O](93)\n", + " [O]C(=O)C([O])=O(94)\n", + " [O]C(=O)O[C]=O(95)\n", + " O=[C]OO[C]=O(96)\n", + " O=C1OOC1=O(97)\n", + " O=CO(98)\n", + " COC(C)=O(99)\n", + " CCOC=O(100)\n", + " CCC(=O)O(101)\n", + " [O]CC([O])=O(102)\n", + " [CH2]OC([O])=O(103)\n", + " [O]CO[C]=O(104)\n", + " [CH2]OO[C]=O(105)\n", + " O=C1COO1(106)\n", + " CO[C]=O(107)\n", + " CC([O])=O(108)\n", + " CCOC(C)=O(109)\n", + " CC(C)OC=O(110)\n", + " CCC(=O)OC(111)\n", + " CCCOC=O(112)\n", + " CC(C)C(=O)O(113)\n", + " CCCC(=O)O(114)\n", + " [CH2]COC=O(115)\n", + " [CH2]CC(=O)O(116)\n", + " CCO[C]=O(117)\n", + " CCC([O])=O(118)\n", + " O=COCO(119)\n", + " O=C(O)CO(120)\n", + " O=[C]OC=O(121)\n", + " [O]C(=O)C=O(122)\n", + " O=CCOC=O(123)\n", + " O=CCC(=O)O(124)\n", + " CC([O])C([O])=O(125)\n", + " C[CH]OC([O])=O(126)\n", + " CC([O])O[C]=O(127)\n", + " C[CH]OO[C]=O(128)\n", + " CC1OOC1=O(129)\n", + " C=COOC=O(130)\n", + " C=COC(=O)O(131)\n", + " [O]C(=O)O(132)\n", + " C=COC=O(133)\n", + " C=CC(=O)O(134)\n", + " [CH2]CC([O])=O(135)\n", + " [CH2]CO[C]=O(136)\n", + " O=C1CCO1(137)\n", + " [CH]COC=O(138)\n", + " [CH]CC(=O)O(139)\n", + " [C]C(140)\n", + " C[CH]O[C]=O(141)\n", + " CC=C([O])[O](142)\n", + " [O]OCOC=O(143)\n", + " [O]OCC(=O)O(144)\n", + " COOO[C]=O(145)\n", + " COOC([O])=O(146)\n", + " CCO(147)\n", + " CCC=O(148)\n", + " CC(C)=O(149)\n", + " O=CC=O(150)\n", + " CC(C)C=O(151)\n", + " CCCC=O(152)\n", + " CCC(C)=O(153)\n", + " [CH2]CC=O(154)\n", + " CC[C]=O(155)\n", + " O=CCO(156)\n", + " O=[C]C=O(157)\n", + " O=CCC=O(158)\n", + " CC(=O)C=O(159)\n", + " [CH-]=[O+]OC=C(160)\n", + " C=C[O+]=[C-]O(161)\n", + " C=CC=O(162)\n", + " [CH]CC=O(163)\n", + " [O]OCC=O(164)\n", + " COO[C]=O(165)\n", + " [O]CC[O](166)\n", + " [CH2]OC[O](167)\n", + " [CH2]OO[CH2](168)\n", + " C1COO1(169)\n", + " [CH2]OC(170)\n", + " [CH2]OCC(171)\n", + " CCC[O](172)\n", + " [CH2]OC=O(173)\n", + " [O]CC=O(174)\n", + " CC([O])C[O](175)\n", + " C[CH]OC[O](176)\n", + " [CH2]OC(C)[O](177)\n", + " [CH2]OO[CH]C(178)\n", + " CC1COO1(179)\n", + " C=COOC(180)\n", + " C=COCO(181)\n", + " [O]CO(182)\n", + " [CH2]CC[O](183)\n", + " [CH2]CO[CH2](184)\n", + " C1COC1(185)\n", + " [CH2]O[CH]C(186)\n", + " C[CH]C[O](187)\n", + " [CH2]OOOC(188)\n", + " COOC[O](189)\n", + " C[CH]OC(190)\n", + " CC(C)[O](191)\n", + " COOC(192)\n", + " CCCC(193)\n", + " C[CH]OCC(194)\n", + " CCC(C)[O](195)\n", + " [CH2]CCC(196)\n", + " C[CH]CC(197)\n", + " CCOOC(198)\n", + " CCOC(199)\n", + " CCCO(200)\n", + " C[CH]OC=O(201)\n", + " CC([O])C=O(202)\n", + " C[CH]C=O(203)\n", + " COOC=O(204)\n", + " CC([O])C(C)[O](205)\n", + " C[CH]OC(C)[O](206)\n", + " C[CH]OO[CH]C(207)\n", + " CC1OOC1C(208)\n", + " C=COOCC(209)\n", + " C=COC(C)O(210)\n", + " CC([O])O(211)\n", + " [CH2]CC(C)[O](212)\n", + " [CH2]CO[CH]C(213)\n", + " CC1CCO1(214)\n", + " C=COCC(215)\n", + " C[CH]O[CH]C(216)\n", + " C[CH]C(C)[O](217)\n", + " C[CH]OOOC(218)\n", + " COOC(C)[O](219)\n", + " [CH2]CO(220)\n", + " COOO(221)\n", + " [CH2]CC[CH2](222)\n", + " [CH2]C[CH]C(223)\n", + " [CH2]COOC(224)\n", + " C[CH]OOC(225)\n", + " COOOOC(226)\n", + " O=C[O][Pt](227)\n", + " O[C]#[Pt](228)\n", + " O[CH]=[Pt](229)\n", + " CC(=O)[O][Pt](230)\n", + " CO[C](=O)[Pt](231)\n", + " O=C(O)[O][Pt](232)\n", + " O=[C]([Pt])OO(233)\n", + " O=CC(=O)[O][Pt](234)\n", + " O=CO[C](=O)[Pt](235)\n", + " O=C=C=O(236)\n", + " O=C=C=O.[Pt](237)\n", + " C=C=O(238)\n", + " C=C=O.[Pt](239)\n", + " O=C=[CH][Pt](240)\n", + " O=C=[C]=[Pt](241)\n", + " OO[C]#[Pt](242)\n", + " O=C[C](=O)[Pt](243)\n", + " C[C]#[Pt](244)\n", + " O=O.[Pt](245)\n", + " O[O][Pt](246)\n", + " C=[C]=[Pt](247)\n", + " O=C[CH]=[Pt](248)\n", + " O=C[C]#[Pt](249)\n", + " OO[CH]=[Pt](250)\n", + " O=CO.[Pt](251)\n", + " O=CC=O.[Pt](252)\n", + "Added 71 new core reactions\n", + " H(12) + CH3(10) <=> CH4(2)\n", + " CH3(10) + CH3(10) <=> C2H6(8)\n", + " H(12) + C2H5(13) <=> C2H6(8)\n", + " H2(6) + CO(7) <=> CH2O(9)\n", + " H(12) + HCO(15) <=> CH2O(9)\n", + " CH3(10) + C2H5(13) <=> C3H8(11)\n", + " H(12) + CH3CH(19) <=> C2H5(13)\n", + " H(12) + C2H4(18) <=> C2H5(13)\n", + " OH(17) + CH3(10) <=> CH3OH(14)\n", + " H(12) + CO(7) <=> HCO(15)\n", + " CO(7) + CH4(2) <=> CH3CHO(16)\n", + " HCO(15) + CH3(10) <=> CH3CHO(16)\n", + " CH3OO(20) <=> O2(3) + CH3(10)\n", + " CH3OO(20) <=> OH(17) + CH2O(9)\n", + " X(1) + CH4(2) <=> CH4X(24)\n", + " X(1) + H2(6) <=> H2X(30)\n", + " X(1) + X(1) + CO2(4) <=> OX(25) + COX(23)\n", + " X(1) + X(1) + H2O(5) <=> HX(21) + OHX(31)\n", + " X(1) + X(1) + C2H6(8) <=> CH3X(27) + CH3X(27)\n", + " X(1) + X(1) + CH2O(9) <=> HX(21) + CHOX(33)\n", + " X(1) + X(1) + CH2O(9) <=> OX(25) + CH2X(26)\n", + " X(1) + X(1) + CH3OH(14) <=> OHX(31) + CH3X(27)\n", + " X(1) + X(1) + CH3CHO(16) <=> CHOX(33) + CH3X(27)\n", + " X(1) + X(1) + C2H4(18) <=> CH2X(26) + CH2X(26)\n", + " X(1) + CH4X(24) <=> HX(21) + CH3X(27)\n", + " X(1) + CHOX(33) <=> OX(25) + CHX(28)\n", + " HCO(15) + CH3(10) <=> CO(7) + CH4(2)\n", + " H(12) + CH4(2) <=> H2(6) + CH3(10)\n", + " CH3(10) + C2H6(8) <=> CH4(2) + C2H5(13)\n", + " HCO(15) + CH4(2) <=> CH2O(9) + CH3(10)\n", + " OH(17) + CH4(2) <=> H2O(5) + CH3(10)\n", + " CH3(10) + C2H5(13) <=> CH4(2) + C2H4(18)\n", + " CH4(2) + CH3CH(19) <=> CH3(10) + C2H5(13)\n", + " OH(17) + HCO(15) <=> H2O(5) + CO(7)\n", + " OH(17) + C2H6(8) <=> H2O(5) + C2H5(13)\n", + " OH(17) + CH2O(9) <=> H2O(5) + HCO(15)\n", + " OH(17) + C2H5(13) <=> H2O(5) + C2H4(18)\n", + " OH(17) + C2H5(13) <=> H2O(5) + CH3CH(19)\n", + " H(12) + HCO(15) <=> H2(6) + CO(7)\n", + " H(12) + C2H6(8) <=> H2(6) + C2H5(13)\n", + " H2(6) + HCO(15) <=> H(12) + CH2O(9)\n", + " H(12) + C2H5(13) <=> H2(6) + C2H4(18)\n", + " H2(6) + CH3CH(19) <=> H(12) + C2H5(13)\n", + " HCO(15) + C2H5(13) <=> CO(7) + C2H6(8)\n", + " HCO(15) + HCO(15) <=> CO(7) + CH2O(9)\n", + " HCO(15) + CH3CH(19) <=> CO(7) + C2H5(13)\n", + " HCO(15) + C2H6(8) <=> CH2O(9) + C2H5(13)\n", + " C2H5(13) + C2H5(13) <=> C2H4(18) + C2H6(8)\n", + " CH3CH(19) + C2H6(8) <=> C2H5(13) + C2H5(13)\n", + " HCO(15) + C2H5(13) <=> CH2O(9) + C2H4(18)\n", + " CH2O(9) + CH3CH(19) <=> HCO(15) + C2H5(13)\n", + " CH3CH(19) + C2H5(13) <=> C2H4(18) + C2H5(13)\n", + " H2X(30) + CHX(28) <=> HX(21) + CH2X(26)\n", + " H2X(30) + CH2X(26) <=> HX(21) + CH3X(27)\n", + " H2X(30) + CX(29) <=> HX(21) + CHX(28)\n", + " OX(25) + H2X(30) <=> HX(21) + OHX(31)\n", + " H2X(30) + COX(23) <=> HX(21) + CHOX(33)\n", + " COX(23) + CH4X(24) <=> CHOX(33) + CH3X(27)\n", + " CHX(28) + CHOX(33) <=> COX(23) + CH2X(26)\n", + " CHOX(33) + CH2X(26) <=> COX(23) + CH3X(27)\n", + " CX(29) + CHOX(33) <=> COX(23) + CHX(28)\n", + " OX(25) + CHOX(33) <=> OHX(31) + COX(23)\n", + " OHX(31) + CHOX(33) <=> H2OX(32) + COX(23)\n", + " OX(25) + CH4X(24) <=> OHX(31) + CH3X(27)\n", + " CH2X(26) + CH4X(24) <=> CH3X(27) + CH3X(27)\n", + " CHX(28) + CH4X(24) <=> CH2X(26) + CH3X(27)\n", + " CX(29) + CH4X(24) <=> CHX(28) + CH3X(27)\n", + " OHX(31) + CH4X(24) <=> H2OX(32) + CH3X(27)\n", + " CH2X(26) + CH2X(26) <=> CHX(28) + CH3X(27)\n", + " CX(29) + CH3X(27) <=> CHX(28) + CH2X(26)\n", + " CX(29) + CH2X(26) <=> CHX(28) + CHX(28)\n", + "Created 566 new edge reactions\n", + " H2(6) + [CH2](52) <=> CH4(2)\n", + " [CH2](52) + CH4(2) <=> C2H6(8)\n", + " H(12) + CH2(42) <=> CH3(10)\n", + " [CH2](52) + C2H6(8) <=> C3H8(11)\n", + " [CH2](52) + C2H6(8) <=> C3H8(11)\n", + " H(12) + C[CH]C(53) <=> C3H8(11)\n", + " H(12) + [CH2]CC(54) <=> C3H8(11)\n", + " CH2(42) + CH3(10) <=> C2H5(13)\n", + " H(12) + [CH2][CH2](55) <=> C2H5(13)\n", + " H2O(5) + [CH2](52) <=> CH3OH(14)\n", + " H(12) + CH3O(44) <=> CH3OH(14)\n", + " H(12) + CH2OH(47) <=> CH3OH(14)\n", + " H(12) + CO(35) <=> HCO(15)\n", + " [CH2]C[O](56) <=> CH3CHO(16)\n", + " C=CO(57) <=> CH3CHO(16)\n", + " H(12) + [CH2]C=O(58) <=> CH3CHO(16)\n", + " H(12) + C[C]=O(59) <=> CH3CHO(16)\n", + " H(12) + [CH]=C(60) <=> C2H4(18)\n", + " [CH]C(61) <=> C2H4(18)\n", + " H(12) + [CH][CH2](62) <=> CH3CH(19)\n", + " HO2(50) + [CH2](52) <=> CH3OO(20)\n", + " O(34) + CH3O(44) <=> CH3OO(20)\n", + " H(12) + [CH2]O[O](63) <=> CH3OO(20)\n", + " X(1) + X(1) + C2H6(8) <=> HX(21) + C[CH2][Pt](64)\n", + " X(1) + C2H6(8) <=> CC.[Pt](65)\n", + " X(1) + X(1) + C3H8(11) <=> CH3X(27) + C[CH2][Pt](64)\n", + " X(1) + X(1) + C3H8(11) <=> HX(21) + C[CH](C)[Pt](66)\n", + " X(1) + X(1) + C3H8(11) <=> HX(21) + CC[CH2][Pt](67)\n", + " X(1) + C3H8(11) <=> CCC.[Pt](68)\n", + " X(1) + C2H5(13) <=> C[CH2][Pt](64)\n", + " X(1) + X(1) + CH3OH(14) <=> HX(21) + CH3O_X(45)\n", + " X(1) + X(1) + CH3OH(14) <=> HX(21) + CH2OH_X(48)\n", + " X(1) + X(1) + CH3CHO(16) <=> HX(21) + O=C[CH2][Pt](69)\n", + " X(1) + X(1) + CH3CHO(16) <=> HX(21) + C[C](=O)[Pt](70)\n", + " X(1) + X(1) + CH3CHO(16) <=> OX(25) + C[CH]=[Pt](71)\n", + " X(1) + CH3CHO(16) <=> CC=O.[Pt](72)\n", + " X(1) + X(1) + C2H4(18) <=> HX(21) + C=[CH][Pt](73)\n", + " X(1) + C2H4(18) <=> C=C.[Pt](74)\n", + " X(1) + CH3OO(20) <=> CO[O][Pt](75)\n", + " O2(3) + CH4(2) <=> HO2(50) + CH3(10)\n", + " CO2(4) + CH4(2) <=> COC=O(76)\n", + " CO2(4) + CH4(2) <=> CC(=O)O(77)\n", + " COOH(36) + CH3(10) <=> CO2(4) + CH4(2)\n", + " HCOO(38) + CH3(10) <=> CO2(4) + CH4(2)\n", + " CH3(10) + CH2OH(47) <=> CH2O(9) + CH4(2)\n", + " CH3(10) + CH3O(44) <=> CH2O(9) + CH4(2)\n", + " [CH]O(78) + CH3(10) <=> HCO(15) + CH4(2)\n", + " CH3(10) + C[CH]O(79) <=> CH4(2) + CH3CHO(16)\n", + " CH3(10) + CC[O](80) <=> CH4(2) + CH3CHO(16)\n", + " CH3OO(20) + CH4(2) <=> CH3(10) + COO(81)\n", + " O2(3) + CO2(4) <=> [O]OO[C]=O(82)\n", + " O2(3) + CO2(4) <=> [O]OC([O])=O(83)\n", + " O2(3) + C2H6(8) <=> HO2(50) + C2H5(13)\n", + " O2(3) + CH2O(9) <=> HO2(50) + HCO(15)\n", + " O2(3) + CH2O(9) <=> [CH2]OO[O](84)\n", + " O2(3) + CH2O(9) <=> [O]CO[O](85)\n", + " HO2(50) + CH2(42) <=> O2(3) + CH3(10)\n", + " O2(3) + C3H8(11) <=> HO2(50) + C[CH]C(53)\n", + " HO2(50) + [CH2]CC(54) <=> O2(3) + C3H8(11)\n", + " O2(3) + C2H5(13) <=> HO2(50) + C2H4(18)\n", + " HO2(50) + CH3CH(19) <=> O2(3) + C2H5(13)\n", + " CCO[O](86) <=> O2(3) + C2H5(13)\n", + " HO2(50) + CH3O(44) <=> O2(3) + CH3OH(14)\n", + " O2(3) + CH3OH(14) <=> HO2(50) + CH2OH(47)\n", + " O2(3) + HCO(15) <=> HO2(50) + CO(7)\n", + " HO2(50) + CO(35) <=> O2(3) + HCO(15)\n", + " O2(3) + HCO(15) <=> [O]OC=O(87)\n", + " HO2(50) + [CH2]C=O(58) <=> O2(3) + CH3CHO(16)\n", + " O2(3) + CH3CHO(16) <=> HO2(50) + C[C]=O(59)\n", + " O2(3) + CH3CHO(16) <=> C[CH]OO[O](88)\n", + " O2(3) + CH3CHO(16) <=> CC([O])O[O](89)\n", + " O2(3) + OH(17) <=> O=O(90) + OH(17)\n", + " O2(3) + C2H4(18) <=> HO2(50) + [CH]=C(60)\n", + " O2(3) + C2H4(18) <=> [CH2]CO[O](91)\n", + " O2(3) + CH3CH(19) <=> C[CH]O[O](92)\n", + " O2(3) + CH3CH(19) <=> HO2(50) + [CH]=C(60)\n", + " HO2(50) + [CH][CH2](62) <=> O2(3) + CH3CH(19)\n", + " O2(3) + CH3OO(20) <=> HO2(50) + [CH2]O[O](63)\n", + " O2(3) + CH3OO(20) <=> COOO[O](93)\n", + " [O]C(=O)C([O])=O(94) <=> CO2(4) + CO2(4)\n", + " [O]C(=O)O[C]=O(95) <=> CO2(4) + CO2(4)\n", + " O=[C]OO[C]=O(96) <=> CO2(4) + CO2(4)\n", + " CO2(4) + CO2(4) <=> O=C1OOC1=O(97)\n", + " OH(17) + COOH(36) <=> H2O(5) + CO2(4)\n", + " OH(17) + HCOO(38) <=> H2O(5) + CO2(4)\n", + " H2(6) + CO2(4) <=> O=CO(98)\n", + " H(12) + COOH(36) <=> H2(6) + CO2(4)\n", + " H(12) + HCOO(38) <=> H2(6) + CO2(4)\n", + " CO2(4) + C2H6(8) <=> COC(C)=O(99)\n", + " CO2(4) + C2H6(8) <=> CCOC=O(100)\n", + " CO2(4) + C2H6(8) <=> CCC(=O)O(101)\n", + " COOH(36) + C2H5(13) <=> CO2(4) + C2H6(8)\n", + " HCOO(38) + C2H5(13) <=> CO2(4) + C2H6(8)\n", + " [O]CC([O])=O(102) <=> CO2(4) + CH2O(9)\n", + " [CH2]OC([O])=O(103) <=> CO2(4) + CH2O(9)\n", + " [O]CO[C]=O(104) <=> CO2(4) + CH2O(9)\n", + " [CH2]OO[C]=O(105) <=> CO2(4) + CH2O(9)\n", + " CO2(4) + CH2O(9) <=> O=C1COO1(106)\n", + " HCO(15) + COOH(36) <=> CO2(4) + CH2O(9)\n", + " HCO(15) + HCOO(38) <=> CO2(4) + CH2O(9)\n", + " COOH(36) + CH2(42) <=> CO2(4) + CH3(10)\n", + " HCOO(38) + CH2(42) <=> CO2(4) + CH3(10)\n", + " CO2(4) + CH3(10) <=> CO[C]=O(107)\n", + " CO2(4) + CH3(10) <=> CC([O])=O(108)\n", + " CO2(4) + C3H8(11) <=> CCOC(C)=O(109)\n", + " CO2(4) + C3H8(11) <=> CC(C)OC=O(110)\n", + " CO2(4) + C3H8(11) <=> CCC(=O)OC(111)\n", + " CO2(4) + C3H8(11) <=> CCCOC=O(112)\n", + " CO2(4) + C3H8(11) <=> CC(C)C(=O)O(113)\n", + " CO2(4) + C3H8(11) <=> CCCC(=O)O(114)\n", + " COOH(36) + C[CH]C(53) <=> CO2(4) + C3H8(11)\n", + " COOH(36) + [CH2]CC(54) <=> CO2(4) + C3H8(11)\n", + " HCOO(38) + C[CH]C(53) <=> CO2(4) + C3H8(11)\n", + " HCOO(38) + [CH2]CC(54) <=> CO2(4) + C3H8(11)\n", + " H(12) + CO2(4) <=> COOH(36)\n", + " H(12) + CO2(4) <=> HCOO(38)\n", + " CO2(4) + C2H5(13) <=> [CH2]COC=O(115)\n", + " CO2(4) + C2H5(13) <=> [CH2]CC(=O)O(116)\n", + " COOH(36) + [CH2][CH2](55) <=> CO2(4) + C2H5(13)\n", + " COOH(36) + CH3CH(19) <=> CO2(4) + C2H5(13)\n", + " HCOO(38) + [CH2][CH2](55) <=> CO2(4) + C2H5(13)\n", + " HCOO(38) + CH3CH(19) <=> CO2(4) + C2H5(13)\n", + " CO2(4) + C2H5(13) <=> CCO[C]=O(117)\n", + " CO2(4) + C2H5(13) <=> CCC([O])=O(118)\n", + " CO2(4) + CH3OH(14) <=> O=COCO(119)\n", + " CO2(4) + CH3OH(14) <=> O=C(O)CO(120)\n", + " COOH(36) + CH3O(44) <=> CO2(4) + CH3OH(14)\n", + " COOH(36) + CH2OH(47) <=> CO2(4) + CH3OH(14)\n", + " HCOO(38) + CH3O(44) <=> CO2(4) + CH3OH(14)\n", + " HCOO(38) + CH2OH(47) <=> CO2(4) + CH3OH(14)\n", + " CO(35) + COOH(36) <=> CO2(4) + HCO(15)\n", + " CO(35) + HCOO(38) <=> CO2(4) + HCO(15)\n", + " CO2(4) + HCO(15) <=> O=[C]OC=O(121)\n", + " CO2(4) + HCO(15) <=> [O]C(=O)C=O(122)\n", + " CO2(4) + CH3CHO(16) <=> O=CCOC=O(123)\n", + " CO2(4) + CH3CHO(16) <=> O=CCC(=O)O(124)\n", + " CC([O])C([O])=O(125) <=> CO2(4) + CH3CHO(16)\n", + " C[CH]OC([O])=O(126) <=> CO2(4) + CH3CHO(16)\n", + " CC([O])O[C]=O(127) <=> CO2(4) + CH3CHO(16)\n", + " C[CH]OO[C]=O(128) <=> CO2(4) + CH3CHO(16)\n", + " CO2(4) + CH3CHO(16) <=> CC1OOC1=O(129)\n", + " COOH(36) + [CH2]C=O(58) <=> CO2(4) + CH3CHO(16)\n", + " COOH(36) + C[C]=O(59) <=> CO2(4) + CH3CHO(16)\n", + " HCOO(38) + [CH2]C=O(58) <=> CO2(4) + CH3CHO(16)\n", + " HCOO(38) + C[C]=O(59) <=> CO2(4) + CH3CHO(16)\n", + " C=COOC=O(130) <=> CO2(4) + CH3CHO(16)\n", + " C=COC(=O)O(131) <=> CO2(4) + CH3CHO(16)\n", + " O(34) + COOH(36) <=> OH(17) + CO2(4)\n", + " O(34) + HCOO(38) <=> OH(17) + CO2(4)\n", + " OH(17) + CO2(4) <=> [O]C(=O)O(132)\n", + " CO2(4) + C2H4(18) <=> C=COC=O(133)\n", + " CO2(4) + C2H4(18) <=> C=CC(=O)O(134)\n", + " [CH2]CC([O])=O(135) <=> CO2(4) + C2H4(18)\n", + " [CH2]CO[C]=O(136) <=> CO2(4) + C2H4(18)\n", + " CO2(4) + C2H4(18) <=> O=C1CCO1(137)\n", + " COOH(36) + [CH]=C(60) <=> CO2(4) + C2H4(18)\n", + " HCOO(38) + [CH]=C(60) <=> CO2(4) + C2H4(18)\n", + " CO2(4) + CH3CH(19) <=> [CH]COC=O(138)\n", + " CO2(4) + CH3CH(19) <=> [CH]CC(=O)O(139)\n", + " COOH(36) + [CH][CH2](62) <=> CO2(4) + CH3CH(19)\n", + " COOH(36) + [C]C(140) <=> CO2(4) + CH3CH(19)\n", + " HCOO(38) + [CH][CH2](62) <=> CO2(4) + CH3CH(19)\n", + " HCOO(38) + [C]C(140) <=> CO2(4) + CH3CH(19)\n", + " CO2(4) + CH3CH(19) <=> C[CH]O[C]=O(141)\n", + " CO2(4) + CH3CH(19) <=> CC=C([O])[O](142)\n", + " CO2(4) + CH3OO(20) <=> [O]OCOC=O(143)\n", + " CO2(4) + CH3OO(20) <=> [O]OCC(=O)O(144)\n", + " COOH(36) + [CH2]O[O](63) <=> CO2(4) + CH3OO(20)\n", + " HCOO(38) + [CH2]O[O](63) <=> CO2(4) + CH3OO(20)\n", + " CO2(4) + CH3OO(20) <=> COOO[C]=O(145)\n", + " CO2(4) + CH3OO(20) <=> COOC([O])=O(146)\n", + " H2O(5) + CO(7) <=> O=CO(98)\n", + " OH(17) + CH2OH(47) <=> H2O(5) + CH2O(9)\n", + " OH(17) + CH3O(44) <=> H2O(5) + CH2O(9)\n", + " OH(17) + [CH]O(78) <=> H2O(5) + HCO(15)\n", + " OH(17) + C[CH]O(79) <=> H2O(5) + CH3CHO(16)\n", + " OH(17) + CC[O](80) <=> H2O(5) + CH3CHO(16)\n", + " H2O(5) + C2H4(18) <=> CCO(147)\n", + " OH(17) + COO(81) <=> H2O(5) + CH3OO(20)\n", + " H(12) + CH2OH(47) <=> H2(6) + CH2O(9)\n", + " H(12) + CH3O(44) <=> H2(6) + CH2O(9)\n", + " H(12) + [CH]O(78) <=> H2(6) + HCO(15)\n", + " H(12) + C[CH]O(79) <=> H2(6) + CH3CHO(16)\n", + " H(12) + CC[O](80) <=> H2(6) + CH3CHO(16)\n", + " H(12) + COO(81) <=> H2(6) + CH3OO(20)\n", + " CO(7) + C2H6(8) <=> CCC=O(148)\n", + " CO(7) + C2H6(8) <=> CC(C)=O(149)\n", + " CO(7) + CH2O(9) <=> O=CC=O(150)\n", + " HCO(15) + CH2(42) <=> CO(7) + CH3(10)\n", + " CO(7) + CH3(10) <=> C[C]=O(59)\n", + " CO(7) + C3H8(11) <=> CC(C)C=O(151)\n", + " CO(7) + C3H8(11) <=> CCCC=O(152)\n", + " CO(7) + C3H8(11) <=> CCC(C)=O(153)\n", + " HCO(15) + C[CH]C(53) <=> CO(7) + C3H8(11)\n", + " HCO(15) + [CH2]CC(54) <=> CO(7) + C3H8(11)\n", + " CO(7) + C2H5(13) <=> [CH2]CC=O(154)\n", + " HCO(15) + [CH2][CH2](55) <=> CO(7) + C2H5(13)\n", + " CC[C]=O(155) <=> CO(7) + C2H5(13)\n", + " CO(7) + CH3OH(14) <=> COC=O(76)\n", + " CO(7) + CH3OH(14) <=> O=CCO(156)\n", + " HCO(15) + CH3O(44) <=> CO(7) + CH3OH(14)\n", + " HCO(15) + CH2OH(47) <=> CO(7) + CH3OH(14)\n", + " CO(35) + HCO(15) <=> CO(7) + HCO(15)\n", + " CO(7) + HCO(15) <=> O=[C]C=O(157)\n", + " CO(7) + CH3CHO(16) <=> O=CCC=O(158)\n", + " CO(7) + CH3CHO(16) <=> CC(=O)C=O(159)\n", + " HCO(15) + [CH2]C=O(58) <=> CO(7) + CH3CHO(16)\n", + " HCO(15) + C[C]=O(59) <=> CO(7) + CH3CHO(16)\n", + " [CH-]=[O+]OC=C(160) <=> CO(7) + CH3CHO(16)\n", + " C=C[O+]=[C-]O(161) <=> CO(7) + CH3CHO(16)\n", + " O(34) + HCO(15) <=> OH(17) + CO(7)\n", + " OH(17) + CO(7) <=> COOH(36)\n", + " CO(7) + C2H4(18) <=> C=CC=O(162)\n", + " HCO(15) + [CH]=C(60) <=> CO(7) + C2H4(18)\n", + " CO(7) + CH3CH(19) <=> [CH]CC=O(163)\n", + " HCO(15) + [CH][CH2](62) <=> CO(7) + CH3CH(19)\n", + " HCO(15) + [C]C(140) <=> CO(7) + CH3CH(19)\n", + " CO(7) + CH3OO(20) <=> [O]OCC=O(164)\n", + " HCO(15) + [CH2]O[O](63) <=> CO(7) + CH3OO(20)\n", + " CO(7) + CH3OO(20) <=> COO[C]=O(165)\n", + " CH2OH(47) + C2H5(13) <=> CH2O(9) + C2H6(8)\n", + " CH3O(44) + C2H5(13) <=> CH2O(9) + C2H6(8)\n", + " [CH]O(78) + C2H5(13) <=> HCO(15) + C2H6(8)\n", + " C2H5(13) + C[CH]O(79) <=> CH3CHO(16) + C2H6(8)\n", + " C2H5(13) + CC[O](80) <=> CH3CHO(16) + C2H6(8)\n", + " CH3OO(20) + C2H6(8) <=> COO(81) + C2H5(13)\n", + " [O]CC[O](166) <=> CH2O(9) + CH2O(9)\n", + " [CH2]OC[O](167) <=> CH2O(9) + CH2O(9)\n", + " [CH2]OO[CH2](168) <=> CH2O(9) + CH2O(9)\n", + " CH2O(9) + CH2O(9) <=> C1COO1(169)\n", + " HCO(15) + CH2OH(47) <=> CH2O(9) + CH2O(9)\n", + " HCO(15) + CH3O(44) <=> CH2O(9) + CH2O(9)\n", + " CH2(42) + CH2OH(47) <=> CH2O(9) + CH3(10)\n", + " CH2(42) + CH3O(44) <=> CH2O(9) + CH3(10)\n", + " CH2O(9) + CH3(10) <=> [CH2]OC(170)\n", + " CH2O(9) + CH3(10) <=> CC[O](80)\n", + " CH2OH(47) + C[CH]C(53) <=> CH2O(9) + C3H8(11)\n", + " CH2OH(47) + [CH2]CC(54) <=> CH2O(9) + C3H8(11)\n", + " CH3O(44) + C[CH]C(53) <=> CH2O(9) + C3H8(11)\n", + " CH3O(44) + [CH2]CC(54) <=> CH2O(9) + C3H8(11)\n", + " H(12) + CH2O(9) <=> CH2OH(47)\n", + " H(12) + CH2O(9) <=> CH3O(44)\n", + " CH2OH(47) + [CH2][CH2](55) <=> CH2O(9) + C2H5(13)\n", + " CH2OH(47) + CH3CH(19) <=> CH2O(9) + C2H5(13)\n", + " CH3O(44) + [CH2][CH2](55) <=> CH2O(9) + C2H5(13)\n", + " CH3O(44) + CH3CH(19) <=> CH2O(9) + C2H5(13)\n", + " CH2O(9) + C2H5(13) <=> [CH2]OCC(171)\n", + " CH2O(9) + C2H5(13) <=> CCC[O](172)\n", + " CH2OH(47) + CH3O(44) <=> CH2O(9) + CH3OH(14)\n", + " CH2OH(47) + CH2OH(47) <=> CH2O(9) + CH3OH(14)\n", + " CH3O(44) + CH3O(44) <=> CH2O(9) + CH3OH(14)\n", + " CH2OH(47) + CH3O(44) <=> CH2O(9) + CH3OH(14)\n", + " HCO(15) + [CH]O(78) <=> HCO(15) + CH2O(9)\n", + " CO(35) + CH2OH(47) <=> HCO(15) + CH2O(9)\n", + " CO(35) + CH3O(44) <=> HCO(15) + CH2O(9)\n", + " HCO(15) + CH2O(9) <=> [CH2]OC=O(173)\n", + " HCO(15) + CH2O(9) <=> [O]CC=O(174)\n", + " CC([O])C[O](175) <=> CH2O(9) + CH3CHO(16)\n", + " C[CH]OC[O](176) <=> CH2O(9) + CH3CHO(16)\n", + " [CH2]OC(C)[O](177) <=> CH2O(9) + CH3CHO(16)\n", + " [CH2]OO[CH]C(178) <=> CH2O(9) + CH3CHO(16)\n", + " CH2O(9) + CH3CHO(16) <=> CC1COO1(179)\n", + " HCO(15) + C[CH]O(79) <=> CH2O(9) + CH3CHO(16)\n", + " HCO(15) + CC[O](80) <=> CH2O(9) + CH3CHO(16)\n", + " CH2OH(47) + [CH2]C=O(58) <=> CH2O(9) + CH3CHO(16)\n", + " CH2OH(47) + C[C]=O(59) <=> CH2O(9) + CH3CHO(16)\n", + " CH3O(44) + [CH2]C=O(58) <=> CH2O(9) + CH3CHO(16)\n", + " CH3O(44) + C[C]=O(59) <=> CH2O(9) + CH3CHO(16)\n", + " C=COOC(180) <=> CH2O(9) + CH3CHO(16)\n", + " C=COCO(181) <=> CH2O(9) + CH3CHO(16)\n", + " O(34) + CH2OH(47) <=> OH(17) + CH2O(9)\n", + " O(34) + CH3O(44) <=> OH(17) + CH2O(9)\n", + " OH(17) + CH2O(9) <=> [O]CO(182)\n", + " [CH2]CC[O](183) <=> CH2O(9) + C2H4(18)\n", + " [CH2]CO[CH2](184) <=> CH2O(9) + C2H4(18)\n", + " CH2O(9) + C2H4(18) <=> C1COC1(185)\n", + " CH2OH(47) + [CH]=C(60) <=> CH2O(9) + C2H4(18)\n", + " CH3O(44) + [CH]=C(60) <=> CH2O(9) + C2H4(18)\n", + " CH2OH(47) + [CH][CH2](62) <=> CH2O(9) + CH3CH(19)\n", + " CH2OH(47) + [C]C(140) <=> CH2O(9) + CH3CH(19)\n", + " CH3O(44) + [CH][CH2](62) <=> CH2O(9) + CH3CH(19)\n", + " CH3O(44) + [C]C(140) <=> CH2O(9) + CH3CH(19)\n", + " CH2O(9) + CH3CH(19) <=> [CH2]O[CH]C(186)\n", + " CH2O(9) + CH3CH(19) <=> C[CH]C[O](187)\n", + " [CH2]O[O](63) + CH2OH(47) <=> CH2O(9) + CH3OO(20)\n", + " [CH2]O[O](63) + CH3O(44) <=> CH2O(9) + CH3OO(20)\n", + " HCO(15) + COO(81) <=> CH2O(9) + CH3OO(20)\n", + " CH2O(9) + CH3OO(20) <=> [CH2]OOOC(188)\n", + " CH2O(9) + CH3OO(20) <=> COOC[O](189)\n", + " CH2(42) + CH4(2) <=> CH3(10) + CH3(10)\n", + " CH4(2) + C[CH]C(53) <=> CH3(10) + C3H8(11)\n", + " CH3(10) + C3H8(11) <=> CH4(2) + [CH2]CC(54)\n", + " H2(6) + CH2(42) <=> H(12) + CH3(10)\n", + " CH2(42) + C2H6(8) <=> CH3(10) + C2H5(13)\n", + " CH3O(44) + CH4(2) <=> CH3(10) + CH3OH(14)\n", + " CH3(10) + CH3OH(14) <=> CH2OH(47) + CH4(2)\n", + " CH2(42) + [CH]O(78) <=> HCO(15) + CH3(10)\n", + " CH2(42) + CH2O(9) <=> HCO(15) + CH3(10)\n", + " CO(35) + CH4(2) <=> HCO(15) + CH3(10)\n", + " CH2(42) + C[CH]O(79) <=> CH3(10) + CH3CHO(16)\n", + " CH2(42) + CC[O](80) <=> CH3(10) + CH3CHO(16)\n", + " CH3(10) + CH3CHO(16) <=> CH4(2) + [CH2]C=O(58)\n", + " CH3(10) + CH3CHO(16) <=> CH4(2) + C[C]=O(59)\n", + " CH3(10) + CH3CHO(16) <=> C[CH]OC(190)\n", + " CH3(10) + CH3CHO(16) <=> CC(C)[O](191)\n", + " OH(17) + CH3(10) <=> H2O(5) + CH2(42)\n", + " O(34) + CH4(2) <=> OH(17) + CH3(10)\n", + " CH2(42) + C2H5(13) <=> CH3(10) + C2H4(18)\n", + " CH4(2) + [CH]=C(60) <=> CH3(10) + C2H4(18)\n", + " CH3(10) + C2H4(18) <=> [CH2]CC(54)\n", + " CH3(10) + CH3CH(19) <=> C[CH]C(53)\n", + " CH3(10) + CH3CH(19) <=> CH4(2) + [CH]=C(60)\n", + " CH2(42) + C2H5(13) <=> CH3(10) + CH3CH(19)\n", + " CH3(10) + CH3CH(19) <=> CH4(2) + [CH][CH2](62)\n", + " CH2(42) + COO(81) <=> CH3(10) + CH3OO(20)\n", + " CH3(10) + CH3OO(20) <=> [CH2]O[O](63) + CH4(2)\n", + " CH3(10) + CH3OO(20) <=> COOC(192)\n", + " H2(6) + C[CH]C(53) <=> H(12) + C3H8(11)\n", + " H(12) + C3H8(11) <=> H2(6) + [CH2]CC(54)\n", + " C2H6(8) + C[CH]C(53) <=> C2H5(13) + C3H8(11)\n", + " C2H6(8) + [CH2]CC(54) <=> C2H5(13) + C3H8(11)\n", + " [CH]O(78) + C[CH]C(53) <=> HCO(15) + C3H8(11)\n", + " [CH]O(78) + [CH2]CC(54) <=> HCO(15) + C3H8(11)\n", + " HCO(15) + C3H8(11) <=> CH2O(9) + C[CH]C(53)\n", + " CH2O(9) + [CH2]CC(54) <=> HCO(15) + C3H8(11)\n", + " C[CH]O(79) + C[CH]C(53) <=> CH3CHO(16) + C3H8(11)\n", + " CC[O](80) + C[CH]C(53) <=> CH3CHO(16) + C3H8(11)\n", + " C[CH]O(79) + [CH2]CC(54) <=> CH3CHO(16) + C3H8(11)\n", + " CC[O](80) + [CH2]CC(54) <=> CH3CHO(16) + C3H8(11)\n", + " OH(17) + C3H8(11) <=> H2O(5) + C[CH]C(53)\n", + " OH(17) + C3H8(11) <=> H2O(5) + [CH2]CC(54)\n", + " C2H5(13) + C[CH]C(53) <=> C2H4(18) + C3H8(11)\n", + " C2H5(13) + [CH2]CC(54) <=> C2H4(18) + C3H8(11)\n", + " CH3CH(19) + C3H8(11) <=> C2H5(13) + C[CH]C(53)\n", + " CH3CH(19) + C3H8(11) <=> C2H5(13) + [CH2]CC(54)\n", + " COO(81) + C[CH]C(53) <=> CH3OO(20) + C3H8(11)\n", + " COO(81) + [CH2]CC(54) <=> CH3OO(20) + C3H8(11)\n", + " H(12) + CH3OH(14) <=> H2(6) + CH3O(44)\n", + " H(12) + CH3OH(14) <=> H2(6) + CH2OH(47)\n", + " H2(6) + CO(35) <=> H(12) + HCO(15)\n", + " H(12) + CH3CHO(16) <=> H2(6) + [CH2]C=O(58)\n", + " H(12) + CH3CHO(16) <=> H2(6) + C[C]=O(59)\n", + " H(12) + CH3CHO(16) <=> C[CH]O(79)\n", + " H(12) + CH3CHO(16) <=> CC[O](80)\n", + " H(12) + C2H4(18) <=> H2(6) + [CH]=C(60)\n", + " H(12) + CH3CH(19) <=> H2(6) + [CH]=C(60)\n", + " H(12) + CH3CH(19) <=> H2(6) + [CH][CH2](62)\n", + " H(12) + CH3OO(20) <=> H2(6) + [CH2]O[O](63)\n", + " H(12) + CH3OO(20) <=> COO(81)\n", + " C2H5(13) + C2H5(13) <=> CCCC(193)\n", + " CH3OH(14) + C2H5(13) <=> CH3O(44) + C2H6(8)\n", + " CH3OH(14) + C2H5(13) <=> CH2OH(47) + C2H6(8)\n", + " [CH]O(78) + [CH2][CH2](55) <=> HCO(15) + C2H5(13)\n", + " [CH]O(78) + CH3CH(19) <=> HCO(15) + C2H5(13)\n", + " CO(35) + C2H6(8) <=> HCO(15) + C2H5(13)\n", + " HCO(15) + C2H5(13) <=> CCC=O(148)\n", + " [CH2][CH2](55) + C[CH]O(79) <=> CH3CHO(16) + C2H5(13)\n", + " [CH2][CH2](55) + CC[O](80) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CH(19) + C[CH]O(79) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CH(19) + CC[O](80) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CHO(16) + C2H5(13) <=> [CH2]C=O(58) + C2H6(8)\n", + " C[C]=O(59) + C2H6(8) <=> CH3CHO(16) + C2H5(13)\n", + " CH3CHO(16) + C2H5(13) <=> C[CH]OCC(194)\n", + " CH3CHO(16) + C2H5(13) <=> CCC(C)[O](195)\n", + " O(34) + C2H6(8) <=> OH(17) + C2H5(13)\n", + " OH(17) + C2H5(13) <=> CCO(147)\n", + " [CH2][CH2](55) + C2H5(13) <=> C2H4(18) + C2H5(13)\n", + " [CH]=C(60) + C2H6(8) <=> C2H4(18) + C2H5(13)\n", + " C2H4(18) + C2H5(13) <=> [CH2]CCC(196)\n", + " CH3CH(19) + C2H5(13) <=> C[CH]CC(197)\n", + " CH3CH(19) + C2H5(13) <=> [CH]=C(60) + C2H6(8)\n", + " [CH][CH2](62) + C2H6(8) <=> CH3CH(19) + C2H5(13)\n", + " CH3OO(20) + C2H5(13) <=> COO(81) + C2H4(18)\n", + " COO(81) + CH3CH(19) <=> CH3OO(20) + C2H5(13)\n", + " [CH2]O[O](63) + C2H6(8) <=> CH3OO(20) + C2H5(13)\n", + " CH3OO(20) + C2H5(13) <=> CCOOC(198)\n", + " [CH]O(78) + CH3O(44) <=> HCO(15) + CH3OH(14)\n", + " [CH]O(78) + CH2OH(47) <=> HCO(15) + CH3OH(14)\n", + " CH2O(9) + CH3O(44) <=> HCO(15) + CH3OH(14)\n", + " CH2O(9) + CH2OH(47) <=> HCO(15) + CH3OH(14)\n", + " CH3O(44) + C[CH]O(79) <=> CH3OH(14) + CH3CHO(16)\n", + " CH3O(44) + CC[O](80) <=> CH3OH(14) + CH3CHO(16)\n", + " CH2OH(47) + C[CH]O(79) <=> CH3OH(14) + CH3CHO(16)\n", + " CH2OH(47) + CC[O](80) <=> CH3OH(14) + CH3CHO(16)\n", + " OH(17) + CH3OH(14) <=> H2O(5) + CH3O(44)\n", + " OH(17) + CH3OH(14) <=> H2O(5) + CH2OH(47)\n", + " CH3OH(14) + C2H4(18) <=> CCOC(199)\n", + " CH3OH(14) + C2H4(18) <=> CCCO(200)\n", + " CH3O(44) + C2H5(13) <=> CH3OH(14) + C2H4(18)\n", + " CH2OH(47) + C2H5(13) <=> CH3OH(14) + C2H4(18)\n", + " CH3OH(14) + CH3CH(19) <=> CH3O(44) + C2H5(13)\n", + " CH3OH(14) + CH3CH(19) <=> CH2OH(47) + C2H5(13)\n", + " CH3O(44) + COO(81) <=> CH3OO(20) + CH3OH(14)\n", + " CH2OH(47) + COO(81) <=> CH3OO(20) + CH3OH(14)\n", + " CO(35) + [CH]O(78) <=> HCO(15) + HCO(15)\n", + " CO(35) + CH2O(9) <=> HCO(15) + HCO(15)\n", + " HCO(15) + HCO(15) <=> O=CC=O(150)\n", + " CO(35) + C[CH]O(79) <=> HCO(15) + CH3CHO(16)\n", + " CO(35) + CC[O](80) <=> HCO(15) + CH3CHO(16)\n", + " [CH]O(78) + [CH2]C=O(58) <=> HCO(15) + CH3CHO(16)\n", + " [CH]O(78) + C[C]=O(59) <=> HCO(15) + CH3CHO(16)\n", + " CH2O(9) + [CH2]C=O(58) <=> HCO(15) + CH3CHO(16)\n", + " CH2O(9) + C[C]=O(59) <=> HCO(15) + CH3CHO(16)\n", + " HCO(15) + CH3CHO(16) <=> C[CH]OC=O(201)\n", + " HCO(15) + CH3CHO(16) <=> CC([O])C=O(202)\n", + " O(34) + [CH]O(78) <=> OH(17) + HCO(15)\n", + " H2O(5) + CO(35) <=> OH(17) + HCO(15)\n", + " O(34) + CH2O(9) <=> OH(17) + HCO(15)\n", + " OH(17) + HCO(15) <=> O=CO(98)\n", + " CO(35) + C2H5(13) <=> HCO(15) + C2H4(18)\n", + " [CH]O(78) + [CH]=C(60) <=> HCO(15) + C2H4(18)\n", + " CH2O(9) + [CH]=C(60) <=> HCO(15) + C2H4(18)\n", + " HCO(15) + C2H4(18) <=> [CH2]CC=O(154)\n", + " HCO(15) + CH3CH(19) <=> C[CH]C=O(203)\n", + " HCO(15) + CH3CH(19) <=> CH2O(9) + [CH]=C(60)\n", + " [CH]O(78) + [CH][CH2](62) <=> HCO(15) + CH3CH(19)\n", + " [CH]O(78) + [C]C(140) <=> HCO(15) + CH3CH(19)\n", + " CO(35) + C2H5(13) <=> HCO(15) + CH3CH(19)\n", + " CH2O(9) + [CH][CH2](62) <=> HCO(15) + CH3CH(19)\n", + " HCO(15) + CH3OO(20) <=> CO(7) + COO(81)\n", + " [CH]O(78) + [CH2]O[O](63) <=> HCO(15) + CH3OO(20)\n", + " CO(35) + COO(81) <=> HCO(15) + CH3OO(20)\n", + " CH2O(9) + [CH2]O[O](63) <=> HCO(15) + CH3OO(20)\n", + " HCO(15) + CH3OO(20) <=> COOC=O(204)\n", + " CC([O])C(C)[O](205) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[CH]OC(C)[O](206) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[CH]OO[CH]C(207) <=> CH3CHO(16) + CH3CHO(16)\n", + " CH3CHO(16) + CH3CHO(16) <=> CC1OOC1C(208)\n", + " [CH2]C=O(58) + C[CH]O(79) <=> CH3CHO(16) + CH3CHO(16)\n", + " [CH2]C=O(58) + CC[O](80) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[C]=O(59) + C[CH]O(79) <=> CH3CHO(16) + CH3CHO(16)\n", + " C[C]=O(59) + CC[O](80) <=> CH3CHO(16) + CH3CHO(16)\n", + " C=COOCC(209) <=> CH3CHO(16) + CH3CHO(16)\n", + " C=COC(C)O(210) <=> CH3CHO(16) + CH3CHO(16)\n", + " O(34) + C[CH]O(79) <=> OH(17) + CH3CHO(16)\n", + " O(34) + CC[O](80) <=> OH(17) + CH3CHO(16)\n", + " OH(17) + CH3CHO(16) <=> H2O(5) + [CH2]C=O(58)\n", + " OH(17) + CH3CHO(16) <=> H2O(5) + C[C]=O(59)\n", + " OH(17) + CH3CHO(16) <=> CC([O])O(211)\n", + " [CH2]CC(C)[O](212) <=> C2H4(18) + CH3CHO(16)\n", + " [CH2]CO[CH]C(213) <=> C2H4(18) + CH3CHO(16)\n", + " C2H4(18) + CH3CHO(16) <=> CC1CCO1(214)\n", + " [CH2]C=O(58) + C2H5(13) <=> C2H4(18) + CH3CHO(16)\n", + " C[C]=O(59) + C2H5(13) <=> C2H4(18) + CH3CHO(16)\n", + " [CH]=C(60) + C[CH]O(79) <=> C2H4(18) + CH3CHO(16)\n", + " [CH]=C(60) + CC[O](80) <=> C2H4(18) + CH3CHO(16)\n", + " C=COCC(215) <=> C2H4(18) + CH3CHO(16)\n", + " [CH][CH2](62) + C[CH]O(79) <=> CH3CH(19) + CH3CHO(16)\n", + " [C]C(140) + C[CH]O(79) <=> CH3CH(19) + CH3CHO(16)\n", + " [CH][CH2](62) + CC[O](80) <=> CH3CH(19) + CH3CHO(16)\n", + " [C]C(140) + CC[O](80) <=> CH3CH(19) + CH3CHO(16)\n", + " CH3CH(19) + CH3CHO(16) <=> [CH2]C=O(58) + C2H5(13)\n", + " CH3CH(19) + CH3CHO(16) <=> C[C]=O(59) + C2H5(13)\n", + " CH3CH(19) + CH3CHO(16) <=> C[CH]O[CH]C(216)\n", + " CH3CH(19) + CH3CHO(16) <=> C[CH]C(C)[O](217)\n", + " [CH2]O[O](63) + C[CH]O(79) <=> CH3OO(20) + CH3CHO(16)\n", + " [CH2]O[O](63) + CC[O](80) <=> CH3OO(20) + CH3CHO(16)\n", + " COO(81) + [CH2]C=O(58) <=> CH3OO(20) + CH3CHO(16)\n", + " COO(81) + C[C]=O(59) <=> CH3OO(20) + CH3CHO(16)\n", + " CH3OO(20) + CH3CHO(16) <=> C[CH]OOOC(218)\n", + " CH3OO(20) + CH3CHO(16) <=> COOC(C)[O](219)\n", + " O(34) + C2H5(13) <=> OH(17) + C2H4(18)\n", + " OH(17) + C2H4(18) <=> H2O(5) + [CH]=C(60)\n", + " OH(17) + C2H4(18) <=> [CH2]CO(220)\n", + " OH(17) + CH3CH(19) <=> C[CH]O(79)\n", + " OH(17) + CH3CH(19) <=> H2O(5) + [CH]=C(60)\n", + " OH(17) + CH3CH(19) <=> O(34) + C2H5(13)\n", + " OH(17) + CH3CH(19) <=> H2O(5) + [CH][CH2](62)\n", + " O(34) + COO(81) <=> OH(17) + CH3OO(20)\n", + " OH(17) + CH3OO(20) <=> H2O(5) + [CH2]O[O](63)\n", + " OH(17) + CH3OO(20) <=> COOO(221)\n", + " [CH2]CC[CH2](222) <=> C2H4(18) + C2H4(18)\n", + " [CH]=C(60) + C2H5(13) <=> C2H4(18) + C2H4(18)\n", + " [CH][CH2](62) + C2H5(13) <=> C2H4(18) + CH3CH(19)\n", + " [C]C(140) + C2H5(13) <=> C2H4(18) + CH3CH(19)\n", + " [CH]=C(60) + C2H5(13) <=> C2H4(18) + CH3CH(19)\n", + " C2H4(18) + CH3CH(19) <=> [CH2]C[CH]C(223)\n", + " [CH2]O[O](63) + C2H5(13) <=> CH3OO(20) + C2H4(18)\n", + " COO(81) + [CH]=C(60) <=> CH3OO(20) + C2H4(18)\n", + " CH3OO(20) + C2H4(18) <=> [CH2]COOC(224)\n", + " CH3CH(19) + CH3CH(19) <=> [CH]=C(60) + C2H5(13)\n", + " CH3CH(19) + CH3CH(19) <=> [CH][CH2](62) + C2H5(13)\n", + " CH3OO(20) + CH3CH(19) <=> C[CH]OOC(225)\n", + " CH3OO(20) + CH3CH(19) <=> COO(81) + [CH]=C(60)\n", + " COO(81) + [CH][CH2](62) <=> CH3OO(20) + CH3CH(19)\n", + " CH3OO(20) + CH3CH(19) <=> [CH2]O[O](63) + C2H5(13)\n", + " [CH2]O[O](63) + COO(81) <=> CH3OO(20) + CH3OO(20)\n", + " CH3OO(20) + CH3OO(20) <=> COOOOC(226)\n", + " HX(21) + CO2X(22) <=> X(1) + O=C[O][Pt](227)\n", + " X(1) + O[C]#[Pt](228) <=> HX(21) + COX(23)\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + CHOX(33)\n", + " O=C[O][Pt](227) + CH3X(27) <=> CO2X(22) + CH4X(24)\n", + " COOH_X(37) + CH3X(27) <=> CO2X(22) + CH4X(24)\n", + " O=C[O][Pt](227) + CH2X(26) <=> CO2X(22) + CH3X(27)\n", + " COOH_X(37) + CH2X(26) <=> CO2X(22) + CH3X(27)\n", + " X(1) + CC(=O)[O][Pt](230) <=> CO2X(22) + CH3X(27)\n", + " X(1) + CO[C](=O)[Pt](231) <=> CO2X(22) + CH3X(27)\n", + " OX(25) + O=C[O][Pt](227) <=> OHX(31) + CO2X(22)\n", + " X(1) + O=C(O)[O][Pt](232) <=> OHX(31) + CO2X(22)\n", + " X(1) + O=[C]([Pt])OO(233) <=> OHX(31) + CO2X(22)\n", + " OHX(31) + O=C[O][Pt](227) <=> H2OX(32) + CO2X(22)\n", + " COX(23) + O=C[O][Pt](227) <=> CO2X(22) + CHOX(33)\n", + " COX(23) + COOH_X(37) <=> CO2X(22) + CHOX(33)\n", + " X(1) + O=CC(=O)[O][Pt](234) <=> CO2X(22) + CHOX(33)\n", + " X(1) + O=CO[C](=O)[Pt](235) <=> CO2X(22) + CHOX(33)\n", + " X(1) + X(1) + O=C=C=O(236) <=> COX(23) + COX(23)\n", + " X(1) + O=C=C=O.[Pt](237) <=> COX(23) + COX(23)\n", + " COX(23) + CH4X(24) <=> HX(21) + C[C](=O)[Pt](70)\n", + " CHX(28) + O[C]#[Pt](228) <=> COX(23) + CH2X(26)\n", + " X(1) + X(1) + C=C=O(238) <=> COX(23) + CH2X(26)\n", + " X(1) + C=C=O.[Pt](239) <=> COX(23) + CH2X(26)\n", + " O[C]#[Pt](228) + CH2X(26) <=> COX(23) + CH3X(27)\n", + " X(1) + C[C](=O)[Pt](70) <=> COX(23) + CH3X(27)\n", + " CX(29) + O[C]#[Pt](228) <=> COX(23) + CHX(28)\n", + " X(1) + O=C=[CH][Pt](240) <=> COX(23) + CHX(28)\n", + " X(1) + O=C=[C]=[Pt](241) <=> CX(29) + COX(23)\n", + " OX(25) + O[C]#[Pt](228) <=> OHX(31) + COX(23)\n", + " X(1) + OO[C]#[Pt](242) <=> OHX(31) + COX(23)\n", + " COX(23) + O[C]#[Pt](228) <=> COX(23) + CHOX(33)\n", + " X(1) + O=C[C](=O)[Pt](243) <=> COX(23) + CHOX(33)\n", + " OX(25) + CH4X(24) <=> HX(21) + CH3O_X(45)\n", + " CH2X(26) + CH4X(24) <=> HX(21) + C[CH2][Pt](64)\n", + " CHX(28) + CH4X(24) <=> HX(21) + C[CH]=[Pt](71)\n", + " CX(29) + CH4X(24) <=> HX(21) + C[C]#[Pt](244)\n", + " CH2O_X(46) + CH3X(27) <=> CHOX(33) + CH4X(24)\n", + " X(1) + O=O.[Pt](245) <=> OX(25) + OX(25)\n", + " X(1) + CH2O_X(46) <=> OX(25) + CH2X(26)\n", + " X(1) + CH3O_X(45) <=> OX(25) + CH3X(27)\n", + " X(1) + O[O][Pt](246) <=> OX(25) + OHX(31)\n", + " OX(25) + H2OX(32) <=> HX(21) + O[O][Pt](246)\n", + " X(1) + O=C[O][Pt](227) <=> OX(25) + CHOX(33)\n", + " X(1) + C=C.[Pt](74) <=> CH2X(26) + CH2X(26)\n", + " X(1) + C[CH2][Pt](64) <=> CH2X(26) + CH3X(27)\n", + " X(1) + C=[CH][Pt](73) <=> CHX(28) + CH2X(26)\n", + " X(1) + C=[C]=[Pt](247) <=> CX(29) + CH2X(26)\n", + " X(1) + CH2OH_X(48) <=> OHX(31) + CH2X(26)\n", + " H2OX(32) + CH2X(26) <=> HX(21) + CH2OH_X(48)\n", + " CHX(28) + O[CH]=[Pt](229) <=> CHOX(33) + CH2X(26)\n", + " CHX(28) + CH2O_X(46) <=> CHOX(33) + CH2X(26)\n", + " X(1) + O=C[CH2][Pt](69) <=> CHOX(33) + CH2X(26)\n", + " X(1) + CC.[Pt](65) <=> CH3X(27) + CH3X(27)\n", + " X(1) + C[CH]=[Pt](71) <=> CHX(28) + CH3X(27)\n", + " X(1) + C[C]#[Pt](244) <=> CX(29) + CH3X(27)\n", + " X(1) + CH3OH_X(43) <=> OHX(31) + CH3X(27)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHOX(33) + CH3X(27)\n", + " CH2X(26) + CH2O_X(46) <=> CHOX(33) + CH3X(27)\n", + " X(1) + CC=O.[Pt](72) <=> CHOX(33) + CH3X(27)\n", + " X(1) + O[CH]=[Pt](229) <=> OHX(31) + CHX(28)\n", + " H2OX(32) + CHX(28) <=> HX(21) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + CHOX(33)\n", + " CX(29) + CH2O_X(46) <=> CHX(28) + CHOX(33)\n", + " X(1) + O=C[CH]=[Pt](248) <=> CHX(28) + CHOX(33)\n", + " X(1) + O[C]#[Pt](228) <=> OHX(31) + CX(29)\n", + " H2OX(32) + CX(29) <=> HX(21) + O[C]#[Pt](228)\n", + " X(1) + O=C[C]#[Pt](249) <=> CX(29) + CHOX(33)\n", + " X(1) + X(1) + H2O2(51) <=> OHX(31) + OHX(31)\n", + " OX(25) + O[CH]=[Pt](229) <=> OHX(31) + CHOX(33)\n", + " OX(25) + CH2O_X(46) <=> OHX(31) + CHOX(33)\n", + " X(1) + X(1) + O=CO(98) <=> OHX(31) + CHOX(33)\n", + " X(1) + OO[CH]=[Pt](250) <=> OHX(31) + CHOX(33)\n", + " X(1) + O=CO.[Pt](251) <=> OHX(31) + CHOX(33)\n", + " OHX(31) + CH2O_X(46) <=> H2OX(32) + CHOX(33)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHOX(33) + CHOX(33)\n", + " COX(23) + CH2O_X(46) <=> CHOX(33) + CHOX(33)\n", + " X(1) + X(1) + O=CC=O(150) <=> CHOX(33) + CHOX(33)\n", + " X(1) + O=CC=O.[Pt](252) <=> CHOX(33) + CHOX(33)\n", + "\n", + "After model enlargement:\n", + " The model core has 36 species and 109 reactions\n", + " The model edge has 219 species and 613 reactions\n", + "\n", + "\n", + "Completed initial enlarge edge step.\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 63 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 63 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 180 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 180 reactions.\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:39\n", + " Memory used: 884.08 MB\n", + "\n", + "Making seed mechanism...\n", + "Beginning model generation stage 1...\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reached max number of objects...preparing to terminate\n", + "At time 1.1710e-09 s, species O[CH]=[Pt](229) at rate ratio 0.10029903578808655 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species O[CH]=[Pt](229) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " O[CH]=[Pt](229)\n", + "Created 0 new edge species\n", + "Moved 8 reactions from edge to core\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + CHOX(33)\n", + " CHX(28) + O[CH]=[Pt](229) <=> CHOX(33) + CH2X(26)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHOX(33) + CH3X(27)\n", + " X(1) + O[CH]=[Pt](229) <=> OHX(31) + CHX(28)\n", + " H2OX(32) + CHX(28) <=> HX(21) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + CHOX(33)\n", + " OX(25) + O[CH]=[Pt](229) <=> OHX(31) + CHOX(33)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHOX(33) + CHOX(33)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 37 species and 117 reactions\n", + " The model edge has 218 species and 605 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For reaction OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229), Ea raised from 48.7 to 53.2 kJ/mol to match endothermicity of reaction.\n", + "For reaction H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229), Ea raised from 143.8 to 146.0 kJ/mol to match endothermicity of reaction.\n", + "For reaction X(1) + O[CH](O)[Pt](260) <=> OHX(31) + O[CH]=[Pt](229), Ea raised from 58.3 to 62.4 kJ/mol to match endothermicity of reaction.\n", + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 11 new edge species\n", + " O=C=CO(253)\n", + " O=C=CO.[Pt](254)\n", + " C[CH](O)[Pt](255)\n", + " C=CO.[Pt](256)\n", + " OC=[CH][Pt](257)\n", + " OC=[C]=[Pt](258)\n", + " OO.[Pt](259)\n", + " O[CH](O)[Pt](260)\n", + " O=C[CH](O)[Pt](261)\n", + " OC=CO(262)\n", + " OC=CO.[Pt](263)\n", + "Added 0 new core reactions\n", + "Created 48 new edge reactions\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + O[C]#[Pt](228)\n", + " H2X(30) + O[C]#[Pt](228) <=> HX(21) + O[CH]=[Pt](229)\n", + " X(1) + CH2OH_X(48) <=> HX(21) + O[CH]=[Pt](229)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHX(28) + COOH_X(37)\n", + " CHOX(33) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " CHX(28) + OO[C]#[Pt](242) <=> COX(23) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " X(1) + X(1) + O=C=CO(253) <=> COX(23) + O[CH]=[Pt](229)\n", + " X(1) + O=C=CO.[Pt](254) <=> COX(23) + O[CH]=[Pt](229)\n", + " O[CH]=[Pt](229) + CH4X(24) <=> CH3X(27) + CH2OH_X(48)\n", + " O[CH]=[Pt](229) + CH4X(24) <=> HX(21) + C[CH](O)[Pt](255)\n", + " O[O][Pt](246) + CHX(28) <=> OX(25) + O[CH]=[Pt](229)\n", + " OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229)\n", + " X(1) + X(1) + O=CO(98) <=> OX(25) + O[CH]=[Pt](229)\n", + " X(1) + O=CO.[Pt](251) <=> OX(25) + O[CH]=[Pt](229)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHX(28) + CH2OH_X(48)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> O[C]#[Pt](228) + CH3X(27)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> CHX(28) + CH2OH_X(48)\n", + " X(1) + X(1) + C=CO(57) <=> CH2X(26) + O[CH]=[Pt](229)\n", + " X(1) + C=CO.[Pt](256) <=> CH2X(26) + O[CH]=[Pt](229)\n", + " CH2X(26) + CH2OH_X(48) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " CHX(28) + CH3OH_X(43) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " O[C]#[Pt](228) + CH4X(24) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " X(1) + C[CH](O)[Pt](255) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " O[C]#[Pt](228) + CH2X(26) <=> CHX(28) + O[CH]=[Pt](229)\n", + " CX(29) + CH2OH_X(48) <=> CHX(28) + O[CH]=[Pt](229)\n", + " X(1) + OC=[CH][Pt](257) <=> CHX(28) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " X(1) + OC=[C]=[Pt](258) <=> CX(29) + O[CH]=[Pt](229)\n", + " H2X(30) + O[CH]=[Pt](229) <=> HX(21) + CH2OH_X(48)\n", + " OHX(31) + O[CH]=[Pt](229) <=> OX(25) + CH2OH_X(48)\n", + " OO.[Pt](259) + CHX(28) <=> OHX(31) + O[CH]=[Pt](229)\n", + " H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229)\n", + " X(1) + O[CH](O)[Pt](260) <=> OHX(31) + O[CH]=[Pt](229)\n", + " H2OX(32) + O[CH]=[Pt](229) <=> OHX(31) + CH2OH_X(48)\n", + " H2OX(32) + O[CH]=[Pt](229) <=> HX(21) + O[CH](O)[Pt](260)\n", + " CHOX(33) + O[CH]=[Pt](229) <=> COX(23) + CH2OH_X(48)\n", + " CHX(28) + OO[CH]=[Pt](250) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[CH]=[Pt](229) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " CHX(28) + O=CO.[Pt](251) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + CH2O_X(46) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " X(1) + O=C[CH](O)[Pt](261) <=> CHOX(33) + O[CH]=[Pt](229)\n", + " O[CH]=[Pt](229) + O[CH]=[Pt](229) <=> CHX(28) + O[CH](O)[Pt](260)\n", + " O[CH]=[Pt](229) + O[CH]=[Pt](229) <=> O[C]#[Pt](228) + CH2OH_X(48)\n", + " O[CH]=[Pt](229) + O[CH]=[Pt](229) <=> CHOX(33) + CH2OH_X(48)\n", + " X(1) + X(1) + OC=CO(262) <=> O[CH]=[Pt](229) + O[CH]=[Pt](229)\n", + " X(1) + OC=CO.[Pt](263) <=> O[CH]=[Pt](229) + O[CH]=[Pt](229)\n", + "\n", + "After model enlargement:\n", + " The model core has 37 species and 117 reactions\n", + " The model edge has 229 species and 653 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 71 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 71 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 228 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 228 reactions.\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:47\n", + " Memory used: 885.65 MB\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reached max number of objects...preparing to terminate\n", + "At time 7.1387e-10 s, species O[C]#[Pt](228) at rate ratio 0.10034976402382545 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species O[C]#[Pt](228) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " O[C]#[Pt](228)\n", + "Created 0 new edge species\n", + "Moved 20 reactions from edge to core\n", + " X(1) + O[C]#[Pt](228) <=> HX(21) + COX(23)\n", + " CHX(28) + O[C]#[Pt](228) <=> COX(23) + CH2X(26)\n", + " O[C]#[Pt](228) + CH2X(26) <=> COX(23) + CH3X(27)\n", + " CX(29) + O[C]#[Pt](228) <=> COX(23) + CHX(28)\n", + " OX(25) + O[C]#[Pt](228) <=> OHX(31) + COX(23)\n", + " COX(23) + O[C]#[Pt](228) <=> COX(23) + CHOX(33)\n", + " X(1) + O[C]#[Pt](228) <=> OHX(31) + CX(29)\n", + " H2OX(32) + CX(29) <=> HX(21) + O[C]#[Pt](228)\n", + " X(1) + O[CH]=[Pt](229) <=> HX(21) + O[C]#[Pt](228)\n", + " H2X(30) + O[C]#[Pt](228) <=> HX(21) + O[CH]=[Pt](229)\n", + " CHOX(33) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[C]#[Pt](228) <=> COX(23) + O[CH]=[Pt](229)\n", + " OHX(31) + O[C]#[Pt](228) <=> OX(25) + O[CH]=[Pt](229)\n", + " CH2X(26) + O[CH]=[Pt](229) <=> O[C]#[Pt](228) + CH3X(27)\n", + " O[C]#[Pt](228) + CH4X(24) <=> O[CH]=[Pt](229) + CH3X(27)\n", + " O[C]#[Pt](228) + CH2X(26) <=> CHX(28) + O[CH]=[Pt](229)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " CX(29) + O[CH]=[Pt](229) <=> CHX(28) + O[C]#[Pt](228)\n", + " H2OX(32) + O[C]#[Pt](228) <=> OHX(31) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[CH]=[Pt](229) <=> CHOX(33) + O[CH]=[Pt](229)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 38 species and 137 reactions\n", + " The model edge has 228 species and 633 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", + "Created 6 new edge species\n", + " O=C=[C](O)[Pt](264)\n", + " C[C](O)=[Pt](265)\n", + " C=[C](O)[Pt](266)\n", + " O[C](O)=[Pt](267)\n", + " O=C[C](O)=[Pt](268)\n", + " OC=[C](O)[Pt](269)\n", + "Added 0 new core reactions\n", + "Created 21 new edge reactions\n", + " CX(29) + COOH_X(37) <=> COX(23) + O[C]#[Pt](228)\n", + " CX(29) + OO[C]#[Pt](242) <=> COX(23) + O[C]#[Pt](228)\n", + " X(1) + O=C=[C](O)[Pt](264) <=> COX(23) + O[C]#[Pt](228)\n", + " O[C]#[Pt](228) + CH4X(24) <=> HX(21) + C[C](O)=[Pt](265)\n", + " O[O][Pt](246) + CX(29) <=> OX(25) + O[C]#[Pt](228)\n", + " X(1) + COOH_X(37) <=> OX(25) + O[C]#[Pt](228)\n", + " CX(29) + CH2OH_X(48) <=> O[C]#[Pt](228) + CH2X(26)\n", + " X(1) + C=[C](O)[Pt](266) <=> O[C]#[Pt](228) + CH2X(26)\n", + " CX(29) + CH3OH_X(43) <=> O[C]#[Pt](228) + CH3X(27)\n", + " X(1) + C[C](O)=[Pt](265) <=> O[C]#[Pt](228) + CH3X(27)\n", + " OO.[Pt](259) + CX(29) <=> OHX(31) + O[C]#[Pt](228)\n", + " X(1) + O[C](O)=[Pt](267) <=> OHX(31) + O[C]#[Pt](228)\n", + " H2OX(32) + O[C]#[Pt](228) <=> HX(21) + O[C](O)=[Pt](267)\n", + " CX(29) + OO[CH]=[Pt](250) <=> CHOX(33) + O[C]#[Pt](228)\n", + " CX(29) + O=CO.[Pt](251) <=> CHOX(33) + O[C]#[Pt](228)\n", + " X(1) + O=C[C](O)=[Pt](268) <=> CHOX(33) + O[C]#[Pt](228)\n", + " CX(29) + O[CH](O)[Pt](260) <=> O[C]#[Pt](228) + O[CH]=[Pt](229)\n", + " CHX(28) + O[C](O)=[Pt](267) <=> O[C]#[Pt](228) + O[CH]=[Pt](229)\n", + " O[C]#[Pt](228) + O[CH]=[Pt](229) <=> COX(23) + CH2OH_X(48)\n", + " X(1) + OC=[C](O)[Pt](269) <=> O[C]#[Pt](228) + O[CH]=[Pt](229)\n", + " CX(29) + O[C](O)=[Pt](267) <=> O[C]#[Pt](228) + O[C]#[Pt](228)\n", + "\n", + "After model enlargement:\n", + " The model core has 38 species and 137 reactions\n", + " The model edge has 234 species and 654 reactions\n", + "\n", + "\n", + "Saving current model core to Chemkin file...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 91 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 91 reactions.\n", + "Saving current model core and edge to Chemkin file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 249 reactions.\n", + "Saving annotated version of Chemkin files...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 249 reactions.\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updating RMG execution statistics...\n", + " Execution time (DD:HH:MM:SS): 00:00:00:54\n", + " Memory used: 885.65 MB\n", + "\n", + "Making seed mechanism...\n", + "Conducting simulation of reaction system 1...\n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reached max number of objects...preparing to terminate\n", + "At time 4.0540e-09 s, species COOH_X(37) at rate ratio 0.10019253368602045 exceeded the minimum rate for moving to model core of 0.1\n", + "terminating simulation due to interrupt...\n", + "\n", + "\n", + "Adding species COOH_X(37) to model core\n", + "\n", + "Summary of Model Enlargement\n", + "---------------------------------\n", + "Added 1 new core species\n", + " COOH_X(37)\n", + "Created 0 new edge species\n", + "Moved 11 reactions from edge to core\n", + " X(1) + COOH_X(37) <=> OHX(31) + COX(23)\n", + " X(1) + COOH_X(37) <=> HX(21) + CO2X(22)\n", + " H2OX(32) + COX(23) <=> HX(21) + COOH_X(37)\n", + " OHX(31) + CO2X(22) <=> OX(25) + COOH_X(37)\n", + " H2OX(32) + CO2X(22) <=> OHX(31) + COOH_X(37)\n", + " COOH_X(37) + CH3X(27) <=> CO2X(22) + CH4X(24)\n", + " COOH_X(37) + CH2X(26) <=> CO2X(22) + CH3X(27)\n", + " COX(23) + COOH_X(37) <=> CO2X(22) + CHOX(33)\n", + " COX(23) + O[CH]=[Pt](229) <=> CHX(28) + COOH_X(37)\n", + " CX(29) + COOH_X(37) <=> COX(23) + O[C]#[Pt](228)\n", + " X(1) + COOH_X(37) <=> OX(25) + O[C]#[Pt](228)\n", + "Added 0 new core reactions\n", + "Created 0 new edge reactions\n", + "\n", + "After model enlargement:\n", + " The model core has 39 species and 148 reactions\n", + " The model edge has 233 species and 643 reactions\n", + "\n", + "\n", + "For reaction generation 1 process is used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating thermo for new species...\n", + "\n", + "Summary of Secondary Model Edge Enlargement\n", + "---------------------------------\n", + "Added 0 new core species\n", "Created 16 new edge species\n", " O=C(O)C(=O)[O][Pt](270)\n", " O=C(O)O[C](=O)[Pt](271)\n", @@ -2710,136 +5840,41 @@ "Saving annotated version of Chemkin files...\n", "Chemkin file contains 545 reactions.\n", "Chemkin file contains 304 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", - "Updating RMG execution statistics...\n", - " Execution time (DD:HH:MM:SS): 00:00:01:21\n", - " Memory used: 886.74 MB\n", - "\n", - "Conducting simulation of reaction system 2...\n", - "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", - "Reached max number of objects...preparing to terminate\n", - "At time 5.4841e-08 s, species [CH]C(61) at rate ratio 0.10142010078648805 exceeded the minimum rate for moving to model core of 0.1\n", - "terminating simulation due to interrupt...\n", - "\n", - "\n", - "Adding species [CH]C(61) to model core\n", - "\n", - "Summary of Model Enlargement\n", - "---------------------------------\n", - "Added 1 new core species\n", - " [CH]C(61)\n", - "Created 0 new edge species\n", - "Moved 1 reactions from edge to core\n", - " [CH]C(61) <=> C2H4(18)\n", - "Added 0 new core reactions\n", - "Created 0 new edge reactions\n", - "\n", - "After model enlargement:\n", - " The model core has 40 species and 149 reactions\n", - " The model edge has 248 species and 697 reactions\n", - "\n", - "\n", - "For reaction generation 1 process is used.\n", - "Generating thermo for new species...\n", - "Warning: Marked reaction X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27) as duplicate of X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27) for saving to Chemkin file.\n", - "\n", - "Summary of Secondary Model Edge Enlargement\n", - "---------------------------------\n", - "Added 0 new core species\n", - "Created 9 new edge species\n", - " [CH][CH2][Pt](286)\n", - " [CH]C.[Pt](287)\n", - " CC1OC1=O(288)\n", - " [CH]COC=O(289)\n", - " [CH]CC(=O)O(290)\n", - " [CH]CC=O(291)\n", - " CC1CO1(292)\n", - " CC1OC1C(293)\n", - " CC1CC1(294)\n", - "Added 3 new core reactions\n", - " X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27)\n", - " X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27)\n", - " X(1) + X(1) + [CH]C(61) <=> CHX(28) + CH3X(27)\n", - "Created 11 new edge reactions\n", - " CH(41) + CH3(10) <=> [CH]C(61)\n", - " X(1) + X(1) + [CH]C(61) <=> HX(21) + [CH][CH2][Pt](286)\n", - " X(1) + X(1) + [CH]C(61) <=> HX(21) + C[C]#[Pt](244)\n", - " X(1) + [CH]C(61) <=> [CH]C.[Pt](287)\n", - " CO2(4) + [CH]C(61) <=> CC1OC1=O(288)\n", - " CO2(4) + [CH]C(61) <=> [CH]COC=O(289)\n", - " CO2(4) + [CH]C(61) <=> [CH]CC(=O)O(290)\n", - " CO(7) + [CH]C(61) <=> [CH]CC=O(291)\n", - " CH2O(9) + [CH]C(61) <=> CC1CO1(292)\n", - " [CH]C(61) + CH3CHO(16) <=> CC1OC1C(293)\n", - " C2H4(18) + [CH]C(61) <=> CC1CC1(294)\n", - "\n", - "After model enlargement:\n", - " The model core has 40 species and 152 reactions\n", - " The model edge has 257 species and 708 reactions\n", - "\n", - "\n", - "Saving current model core to Chemkin file...\n", - "Chemkin file contains 47 reactions.\n", - "Chemkin file contains 105 reactions.\n", - "Saving annotated version of Chemkin files...\n", - "Chemkin file contains 47 reactions.\n", - "Chemkin file contains 105 reactions.\n", - "Saving current model core and edge to Chemkin file...\n", - "Chemkin file contains 553 reactions.\n", - "Chemkin file contains 310 reactions.\n", - "Saving annotated version of Chemkin files...\n", - "Chemkin file contains 553 reactions.\n", - "Chemkin file contains 310 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Updating RMG execution statistics...\n", - " Execution time (DD:HH:MM:SS): 00:00:01:43\n", - " Memory used: 888.25 MB\n", + " Execution time (DD:HH:MM:SS): 00:00:01:08\n", + " Memory used: 885.65 MB\n", "\n", "Making seed mechanism...\n", "Conducting simulation of reaction system 1...\n", - "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", - "At time 6.6507e-06 s, reached target termination RateRatio: 0.0497658467962582\n", - " CH4(2) conversion: 0.2206 \n", - "\n", - "\n", - "For reaction generation 1 process is used.\n", - "\n", - "Summary of Secondary Model Edge Enlargement\n", - "---------------------------------\n", - "Added 0 new core species\n", - "Created 0 new edge species\n", - "Added 0 new core reactions\n", - "Created 0 new edge reactions\n", - "\n", - "After model enlargement:\n", - " The model core has 40 species and 152 reactions\n", - " The model edge has 257 species and 708 reactions\n", - "\n", - "\n", - "Saving current model core to Chemkin file...\n", - "Chemkin file contains 47 reactions.\n", - "Chemkin file contains 105 reactions.\n", - "Saving annotated version of Chemkin files...\n", - "Chemkin file contains 47 reactions.\n", - "Chemkin file contains 105 reactions.\n", - "Saving current model core and edge to Chemkin file...\n", - "Chemkin file contains 553 reactions.\n", - "Chemkin file contains 310 reactions.\n", - "Saving annotated version of Chemkin files...\n", - "Chemkin file contains 553 reactions.\n", - "Chemkin file contains 310 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", - "Updating RMG execution statistics...\n", - " Execution time (DD:HH:MM:SS): 00:00:02:32\n", - " Memory used: 894.27 MB\n", - "\n", - "Conducting simulation of reaction system 2...\n", - "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n", - "At time 4.7641e-06 s, reached target termination RateRatio: 0.04974422721809749\n", - " CH4(2) conversion: 0.8567 \n", + "Warning: Pressure may be varying, but using initial pressure to evaluate k(T,P) expressions!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "At time 6.5626e-06 s, reached target termination RateRatio: 0.04984585295333993\n", + " CH4(2) conversion: 0.7304 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "\n", "For reaction generation 1 process is used.\n", @@ -2852,38 +5887,56 @@ "Created 0 new edge reactions\n", "\n", "After model enlargement:\n", - " The model core has 40 species and 152 reactions\n", - " The model edge has 257 species and 708 reactions\n", + " The model core has 39 species and 148 reactions\n", + " The model edge has 249 species and 698 reactions\n", "\n", "\n", "Saving current model core to Chemkin file...\n", - "Chemkin file contains 47 reactions.\n", - "Chemkin file contains 105 reactions.\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", "Saving annotated version of Chemkin files...\n", - "Chemkin file contains 47 reactions.\n", - "Chemkin file contains 105 reactions.\n", + "Chemkin file contains 46 reactions.\n", + "Chemkin file contains 102 reactions.\n", "Saving current model core and edge to Chemkin file...\n", - "Chemkin file contains 553 reactions.\n", - "Chemkin file contains 310 reactions.\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", "Saving annotated version of Chemkin files...\n", - "Chemkin file contains 553 reactions.\n", - "Chemkin file contains 310 reactions.\n", - "Saving current model core to HTML file...\n", - "Saving current model edge to HTML file...\n", + "Chemkin file contains 545 reactions.\n", + "Chemkin file contains 304 reactions.\n", + "Saving current model core to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving current model edge to HTML file...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Updating RMG execution statistics...\n", - " Execution time (DD:HH:MM:SS): 00:00:03:08\n", - " Memory used: 897.90 MB\n", + " Execution time (DD:HH:MM:SS): 00:00:01:49\n", + " Memory used: 889.14 MB\n", "\n", - "Making seed mechanism...\n", + "Making seed mechanism...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Performing final model checks...\n", "No collision rate violators found in the model's core.\n", "\n", "MODEL GENERATION COMPLETED\n", "\n", - "The final model core has 40 species and 152 reactions\n", - "The final model edge has 257 species and 708 reactions\n", + "The final model core has 39 species and 148 reactions\n", + "The final model edge has 249 species and 698 reactions\n", "\n", - "RMG execution terminated at Tue Apr 7 16:48:07 2026\n" + "RMG execution terminated at Fri Apr 10 09:35:26 2026\n" ] }, { @@ -2892,7 +5945,7 @@ "0" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -2906,7 +5959,7 @@ "input_file = \"\"\"\n", "# Data sources\n", "database(\n", - " thermoLibraries=['covDepSurfaceThermoPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", + " thermoLibraries=['surfaceThermoCovDepPt111', 'surfaceThermoPt111', 'primaryThermoLibrary', 'thermo_DFT_CCSDTF12_BAC','DFT_QCI_thermo'],\n", " reactionLibraries = [\n", " ('Surface/Methane/Deutschmann_Pt', False),\n", " ('Surface/Methane/Vlachos_Pt111', False),\n", @@ -3202,25 +6255,7 @@ ")\n", "\n", "surfaceReactor(\n", - " temperature=(600,'K'),\n", - " initialPressure=(1.0, 'bar'),\n", - " initialGasMoleFractions={\n", - " \"CH4\": 0.108574,\n", - " \"O2\": 0.02088,\n", - " \"Ar\": 0.78547,\n", - " },\n", - " initialSurfaceCoverages={\n", - " \"X\": 1.0,\n", - " },\n", - " surfaceVolumeRatio=(1.e5, 'm^-1'),\n", - " terminationConversion = { \"CH4\":0.95,},\n", - " terminationTime=(10., 's'),\n", - " # terminationConversion={'O2': 0.99,},\n", - " terminationRateRatio=0.05\n", - ")\n", - "\n", - "surfaceReactor(\n", - " temperature=(2000,'K'),\n", + " temperature=(1000,'K'),\n", " initialPressure=(1.0, 'bar'),\n", " initialGasMoleFractions={\n", " \"CH4\": 0.041866,\n", @@ -3246,19 +6281,9 @@ "model(\n", " toleranceKeepInEdge=0.0,\n", " toleranceMoveToCore=1e-1,\n", - "# inturrupt tolerance was 0.1 wout pruning, 1e8 w pruning on\n", " toleranceInterruptSimulation=1e8,\n", " maximumEdgeSpecies=500000,\n", - " maxNumSpecies=44,\n", - "# PRUNING: uncomment to prune\n", - "# minCoreSizeForPrune=50,\n", - "# prune before simulation based on thermo\n", - "# toleranceThermoKeepSpeciesInEdge=0.5,\n", - "# prune rxns from edge that dont move into core\n", - "# minSpeciesExistIterationsForPrune=2,\n", - "# FILTERING: set so threshold is slightly larger than max rate constants\n", - "# filterReactions=True,\n", - "# filterThreshold=5e8, # default value\n", + " maxNumSpecies=40,\n", ")\n", "\n", "options(\n", @@ -3305,7 +6330,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "id": "ad06f6c3", "metadata": {}, "outputs": [ @@ -3313,15 +6338,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Base mechanism contains 23 gas species and 17 surface species\n", - "Coverage-dependent mechanism contains 23 gas species and 17 surface species\n" + "Base mechanism contains 22 gas species and 17 surface species\n", + "Coverage-dependent mechanism contains 22 gas species and 17 surface species\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_180850/4041569244.py:4: UserWarning: StickingRate::validate: \n", + "/tmp/ipykernel_343256/4041569244.py:4: UserWarning: StickingRate::validate: \n", "Sticking coefficient is greater than 1 for reaction 'H(12) + X(1) <=> HX(21)'\n", "at T = 200.0\n", "at T = 500.0\n", @@ -3331,7 +6356,7 @@ "at T = 10000.0\n", "\n", " surf_base = ct.Interface(base_mech_yaml, 'SURF0', [gas_base])\n", - "/tmp/ipykernel_180850/4041569244.py:4: UserWarning: StickingRate::validate: \n", + "/tmp/ipykernel_343256/4041569244.py:4: UserWarning: StickingRate::validate: \n", "Sticking coefficient is greater than 1 for reaction 'OH(17) + X(1) <=> OHX(31)'\n", "at T = 200.0\n", "at T = 500.0\n", @@ -3341,7 +6366,7 @@ "at T = 10000.0\n", "\n", " surf_base = ct.Interface(base_mech_yaml, 'SURF0', [gas_base])\n", - "/tmp/ipykernel_180850/4041569244.py:10: UserWarning: StickingRate::validate: \n", + "/tmp/ipykernel_343256/4041569244.py:10: UserWarning: StickingRate::validate: \n", "Sticking coefficient is greater than 1 for reaction 'H(12) + X(1) <=> HX(21)'\n", "at T = 200.0\n", "at T = 500.0\n", @@ -3351,7 +6376,7 @@ "at T = 10000.0\n", "\n", " surf_cov_dep = ct.Interface(cov_dep_mech_yaml, 'SURF0', [gas_cov_dep])\n", - "/tmp/ipykernel_180850/4041569244.py:10: UserWarning: StickingRate::validate: \n", + "/tmp/ipykernel_343256/4041569244.py:10: UserWarning: StickingRate::validate: \n", "Sticking coefficient is greater than 1 for reaction 'OH(17) + X(1) <=> OHX(31)'\n", "at T = 200.0\n", "at T = 500.0\n", @@ -3453,15 +6478,23 @@ "surf_concentrations_cov_dep = np.array(surf_concentrations_cov_dep)\n" ] }, + { + "cell_type": "markdown", + "id": "a636e9df-fa0b-461e-ab18-5e63f6154917", + "metadata": {}, + "source": [ + "# Coverage-dependent thermo results in less CO on the surface" + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "id": "868aa235", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3472,7 +6505,6 @@ ], "source": [ "# Plot the results\n", - "\n", "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n", "\n", "# Plot COX, X, OX, HX\n", @@ -3493,20 +6525,18 @@ "plt.title('Surface Coverages vs Time')\n", "plt.legend()\n", "plt.xscale('log')\n", - "# plt.yscale('log')\n", "plt.savefig('cross_surface_coverages.png')\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 5, "id": "6dfaabf1", "metadata": {}, "outputs": [], "source": [ - "# Plot the enthalpy of XCO versus coverage\n", - "\n", + "# Compute the enthalpy of XCO versus coverage\n", "i_XCO_base = get_i_thing({'C': 1, 'O': 1, 'X': 1}, surf_base)\n", "i_XCO_cov_dep = get_i_thing({'C': 1, 'O': 1, 'X': 1}, surf_cov_dep)\n", "\n", @@ -3533,25 +6563,33 @@ "\n" ] }, + { + "cell_type": "markdown", + "id": "18ad38fb-7683-4398-824a-08084187e89b", + "metadata": {}, + "source": [ + "# Enthalpy with coverage dependence corrections fits the DFT data pretty well" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "f28971e5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -3561,10 +6599,9 @@ } ], "source": [ - "plt.plot(coverages, enthalpies_base, label='Base', color=colors[0])\n", + "plt.plot(coverages, enthalpies_base, label='Baseline', color=colors[0])\n", "plt.plot(coverages, enthalpies_cov_dep, label='Coverage Dependent', color=colors[1])\n", "\n", - "\n", "# these values copied from Cantera example: https://cantera.org/dev/examples/python/thermo/coverage_dependent_surf.html\n", "# provide discrete enthalpy and entropy values calculated with DFT\n", "# array of CO* coverage\n", @@ -3582,25 +6619,39 @@ "plt.legend()" ] }, + { + "cell_type": "markdown", + "id": "be7dafe4-bab7-4eb1-8dc8-c6436d2bfdca", + "metadata": {}, + "source": [ + "# Entropy with coverage dependence corrections matches the shape of DFT, but has an offset\n", + "\n", + "## This is because we took the XCO coverage-dependence parameters from Bae et al., but use the more recent NASA polynomials by Kreitz et al.\n", + "\n", + "https://pubs.acs.org/doi/full/10.1021/acs.jcim.4c02167 - Bae et al.\n", + "\n", + "https://pubs.acs.org/doi/10.1021/acscatal.2c03378 - Kreitz et al." + ] + }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 7, "id": "8ef85c64", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 54, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -3610,7 +6661,7 @@ } ], "source": [ - "plt.plot(coverages, entropies_base, label='Base', color=colors[0])\n", + "plt.plot(coverages, entropies_base, label='Baseline', color=colors[0])\n", "plt.plot(coverages, entropies_cov_dep, label='Coverage Dependent', color=colors[1])\n", "\n", "# these values copied from Cantera example: https://cantera.org/dev/examples/python/thermo/coverage_dependent_surf.html\n", @@ -3630,40 +6681,97 @@ "plt.legend()\n" ] }, + { + "cell_type": "markdown", + "id": "adff59e6-071b-4d3b-8ae7-88f6df1f2be9", + "metadata": {}, + "source": [ + "## When you subtract the difference in entropy from the Bae and Kreitz thermo at the low-coverage limit, the entropy matches the DFT" + ] + }, + { + "cell_type": "markdown", + "id": "bceeb421-a936-49d6-a344-dea45ca78e3b", + "metadata": {}, + "source": [ + "### Get the entropy offset between Bae and Kreitz at the low-coverage limit" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "06312712", + "execution_count": 8, + "id": "cc012baf-9d02-49df-a7b2-38feab57a693", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "phase_bae = ct.Interface('example_data/covdepsurf.yaml', 'covdep_poly')\n", + "phase_bae.TP = 300, ct.one_atm\n", + "i_XCO_bae = get_i_thing({'Pt': 1, 'C': 1, 'O': 1}, phase_bae)\n", + "phase_bae.standard_entropies_R[i_XCO_bae]\n", + "entropy_offset = phase_bae.standard_entropies_R[i_XCO_bae] - entropies_cov_dep[0]" + ] + }, + { + "cell_type": "markdown", + "id": "72aa3d9b-781b-4f1f-abd0-1cbc4a790318", + "metadata": {}, + "source": [ + "### After applying offset, the entropy matches the DFT" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "a846327b", + "execution_count": 9, + "id": "06312712", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(coverages, entropies_base, label='Baseline', color=colors[0])\n", + "plt.plot(coverages, entropies_cov_dep, label='Coverage Dependent', color=colors[1])\n", + "plt.plot(coverages, entropies_cov_dep + entropy_offset, label='Coverage Dependent + Entropy Offset', color=colors[1], linestyle='dashed')\n", + "\n", + "# these values copied from Cantera example: https://cantera.org/dev/examples/python/thermo/coverage_dependent_surf.html\n", + "# provide discrete enthalpy and entropy values calculated with DFT\n", + "# array of CO* coverage\n", + "dft_covs = [0., 0.11, 0.22, 0.33, 0.44, 0.56, 0.67, 0.78, 0.89, 1.0]\n", + "\n", + "# array of nondimensionalized DFT-derived CO* entropy values\n", + "dft_srs = [2.46, 2.46, 3.79, 3.28, 3.14, 2.41, 2.19, 1.63, 1.6 , 1.12]\n", + "\n", + "plt.scatter(dft_covs, dft_srs, label='DFT', color=colors[2], marker='x')\n", + "\n", + "\n", + "plt.xlabel('Coverage of XCO')\n", + "plt.ylabel('Standard Entropy / R')\n", + "plt.title('Standard Entropy of XCO vs Coverage')\n", + "plt.legend()" + ] } ], "metadata": { - "kernelspec": { - "display_name": "rmg_env", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.23" + "name": "python" } }, "nbformat": 4,