diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..846184e09 --- /dev/null +++ b/.gitignore @@ -0,0 +1,12 @@ +/tools/python/NNTSSD/RPBE-D3_1_split_data +/tools/python/NNTSSD/RPBE-D3_2_selected_13_each +/tools/python/NNTSSD/RPBE-D3_val_1_selected_6_each +/tools/python/NNTSSD/RPBE-D3_val_2_Trainiert_von_VSC +/tools/python/NNTSSD/RPBE-D3_val_3_Trainiert_prep4val +/tools/python/NNTSSD/Z_vonVSC_ext-test_predict_teil +/tools/python/NNTSSD/Z_water_RPBE-D3_70_700_7000 +/tools/python/NNTSSD/Z_water_RPBE-D3_testing +/tools/python/NNTSSD/Z_water_RPBE-D3_trainiert_ext-test +__pycache__/ +Output/ +submit.slrm diff --git a/examples/nnp-dataset/H2O_RPBE-D3/energy.comp b/examples/nnp-dataset/H2O_RPBE-D3/energy.comp new file mode 100644 index 000000000..8400942c7 --- /dev/null +++ b/examples/nnp-dataset/H2O_RPBE-D3/energy.comp @@ -0,0 +1,35 @@ +################################################################################ +# Energy comparison. +################################################################################ +# Col Name Description +################################################################################ +# 1 index Structure index. +# 2 N Number of atoms in structure. +# 3 Eref_phys Reference potential energy (physical units, atomic energy offsets added). +# 4 Ennp_phys NNP potential energy (physical units, atomic energy offsets added). +# 5 Eref_int Reference potential energy (internal units). +# 6 Ennp_int NNP potential energy (internal units). +######################################################################################### +# 1 2 3 4 5 6 +# index N Eref_phys Ennp_phys Eref_int Ennp_int +######################################################################################### + 0 192 -4.90037328E+03 -4.90037247E+03 -6.68092585E+01 -6.66125046E+01 + 1 48 -1.22507199E+03 -1.22507011E+03 -1.15272744E+01 -1.10694394E+01 + 2 48 -1.22506986E+03 -1.22507022E+03 -1.10081378E+01 -1.10965350E+01 + 3 48 -1.22502203E+03 -1.22502119E+03 5.95827955E-01 8.01478245E-01 + 4 48 -1.22505218E+03 -1.22505220E+03 -6.71902847E+00 -6.72310934E+00 + 5 48 -1.22506198E+03 -1.22506140E+03 -9.09775124E+00 -8.95720185E+00 + 6 48 -1.22503158E+03 -1.22502838E+03 -1.72088369E+00 -9.43022153E-01 + 7 48 -1.22500423E+03 -1.22500324E+03 4.91709424E+00 5.15686403E+00 + 8 48 -1.22496473E+03 -1.22496515E+03 1.45013635E+01 1.44003183E+01 + 9 48 -1.22495153E+03 -1.22495439E+03 1.77048257E+01 1.70098142E+01 + 10 48 -1.22494069E+03 -1.22493883E+03 2.03344660E+01 2.07854856E+01 + 11 48 -1.22496274E+03 -1.22496063E+03 1.49838564E+01 1.54963594E+01 + 12 96 -2.45009917E+03 -2.45009678E+03 -1.21796134E+01 -1.15977362E+01 + 13 192 -4.90020436E+03 -4.90020013E+03 -2.58195517E+01 -2.47915608E+01 + 14 192 -4.90003217E+03 -4.90003007E+03 1.59636306E+01 1.64751870E+01 + 15 96 -2.45009730E+03 -2.45009937E+03 -1.17257032E+01 -1.22270407E+01 + 16 192 -4.90039158E+03 -4.90039010E+03 -7.12491576E+01 -7.08887079E+01 + 17 192 -4.89966762E+03 -4.89966767E+03 1.04425112E+02 1.04412480E+02 + 18 192 -4.89968758E+03 -4.89969068E+03 9.95831661E+01 9.88296431E+01 + 19 192 -4.90014395E+03 -4.90014767E+03 -1.11601885E+01 -1.20623448E+01 diff --git a/examples/nnp-dataset/H2O_RPBE-D3/forces.comp b/examples/nnp-dataset/H2O_RPBE-D3/forces.comp new file mode 100644 index 000000000..88161f3cc --- /dev/null +++ b/examples/nnp-dataset/H2O_RPBE-D3/forces.comp @@ -0,0 +1,6207 @@ +################################################################################ +# Force comparison. +################################################################################ +# Col Name Description +################################################################################ +# 1 index_s Structure index. +# 2 index_a Atom index (x, y, z components in consecutive lines). +# 3 Fref_phys Reference force (physical units). +# 4 Fnnp_phys NNP force (physical units). +# 5 Fref_int Reference force (internal units). +# 6 Fnnp_int NNP force (internal units). +######################################################################################### +# 1 2 3 4 5 6 +# index_s index_a Fref_phys Fnnp_phys Fref_int Fnnp_int +######################################################################################### + 0 0 -6.53013700E-03 -5.52018089E-03 -2.73023596E-01 -2.30797552E-01 + 0 0 -2.85245900E-03 -2.55747558E-03 -1.19260685E-01 -1.06927493E-01 + 0 0 -2.79925540E-02 -2.72759232E-02 -1.17036254E+00 -1.14040037E+00 + 0 1 4.51748800E-03 4.45302177E-03 1.88875183E-01 1.86179864E-01 + 0 1 -5.17262000E-04 -5.87236582E-04 -2.16266108E-02 -2.45522327E-02 + 0 1 5.05257300E-03 4.87879000E-03 2.11246969E-01 2.03981140E-01 + 0 2 9.58150000E-05 -2.56188543E-04 4.00600414E-03 -1.07111868E-02 + 0 2 2.21126400E-03 2.44514067E-03 9.24524629E-02 1.02230795E-01 + 0 2 2.34275760E-02 2.28215838E-02 9.79501815E-01 9.54165415E-01 + 0 3 -2.31046000E-04 -8.72809582E-04 -9.65998260E-03 -3.64919772E-02 + 0 3 -7.15091400E-03 -6.93552949E-03 -2.98978146E-01 -2.89972967E-01 + 0 3 2.69447230E-02 2.70738301E-02 1.12655296E+00 1.13195090E+00 + 0 4 -1.53522300E-03 -1.44551153E-03 -6.41873370E-02 -6.04365200E-02 + 0 4 2.79053200E-03 2.99220819E-03 1.16671531E-01 1.25103568E-01 + 0 4 -1.83910350E-02 -1.79691913E-02 -7.68925140E-01 -7.51287948E-01 + 0 5 2.78893800E-03 3.21461388E-03 1.16604886E-01 1.34402301E-01 + 0 5 6.27237000E-03 5.79847712E-03 2.62246414E-01 2.42433057E-01 + 0 5 -6.17103500E-03 -6.84916238E-03 -2.58009620E-01 -2.86361977E-01 + 0 6 1.01715880E-02 8.72681723E-03 4.25271864E-01 3.64866315E-01 + 0 6 1.60127380E-02 1.58316197E-02 6.69489064E-01 6.61916549E-01 + 0 6 3.50991190E-02 3.55335581E-02 1.46748647E+00 1.48565027E+00 + 0 7 -8.30116200E-03 -7.40104170E-03 -3.47069763E-01 -3.09435931E-01 + 0 7 -2.48995000E-02 -2.40918821E-02 -1.04104263E+00 -1.00727631E+00 + 0 7 -3.03949510E-02 -3.02011344E-02 -1.27080624E+00 -1.26270281E+00 + 0 8 -6.69776100E-03 -6.29839481E-03 -2.80031919E-01 -2.63334506E-01 + 0 8 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-7.15528824E-02 -5.06683825E-02 + 0 13 1.02449350E-02 9.94085019E-03 4.28338486E-01 4.15624766E-01 + 0 13 -2.77444400E-03 -2.59109537E-03 -1.15998895E-01 -1.08333129E-01 + 0 14 1.46354040E-02 1.44840549E-02 6.11903032E-01 6.05575159E-01 + 0 14 -6.67066900E-03 -6.81843261E-03 -2.78899208E-01 -2.85077172E-01 + 0 14 -4.52788800E-03 -4.45979018E-03 -1.89310004E-01 -1.86462850E-01 + 0 15 5.35962800E-03 6.09615964E-03 2.24084871E-01 2.54879098E-01 + 0 15 2.47492950E-02 2.38930628E-02 1.03476260E+00 9.98963717E-01 + 0 15 -5.33222200E-03 -4.74304279E-03 -2.22939033E-01 -1.98305579E-01 + 0 16 -1.29067740E-02 -1.34004274E-02 -5.39629391E-01 -5.60268930E-01 + 0 16 -2.63891140E-02 -2.52639749E-02 -1.10332307E+00 -1.05628125E+00 + 0 16 -1.32626900E-03 -1.85933153E-03 -5.54510161E-02 -7.77382435E-02 + 0 17 6.74341300E-03 6.38006182E-03 2.81940619E-01 2.66748986E-01 + 0 17 3.87991300E-03 4.26927883E-03 1.62218312E-01 1.78497612E-01 + 0 17 -4.68813000E-04 -8.16855715E-04 -1.96009687E-02 -3.41525583E-02 + 0 18 -1.19721000E-04 7.06466190E-06 -5.00550876E-03 2.95371965E-04 + 0 18 8.26633000E-03 8.04596205E-03 3.45613445E-01 3.36399909E-01 + 0 18 6.53025100E-03 7.68527223E-03 2.73028362E-01 3.21319547E-01 + 0 19 1.40603000E-04 -3.46251212E-04 5.87858060E-03 -1.44766873E-02 + 0 19 -1.11648360E-02 -1.19585304E-02 -4.66799345E-01 -4.99983533E-01 + 0 19 -1.37518900E-03 -2.22013301E-03 -5.74963505E-02 -9.28232742E-02 + 0 20 -2.30002000E-03 -2.25056762E-03 -9.61633318E-02 -9.40957388E-02 + 0 20 -2.31651000E-04 1.85545897E-03 -9.68527751E-03 7.75763328E-02 + 0 20 -7.82617400E-03 -8.48329773E-03 -3.27210619E-01 -3.54684818E-01 + 0 21 6.52469800E-03 6.86427916E-03 2.72796193E-01 2.86994006E-01 + 0 21 -2.85405010E-02 -2.91583950E-02 -1.19327209E+00 -1.21910610E+00 + 0 21 -5.26442600E-03 -5.02950232E-03 -2.20104497E-01 -2.10282389E-01 + 0 22 -3.17073020E-02 -3.18361043E-02 -1.32567534E+00 -1.33106054E+00 + 0 22 2.61348280E-02 2.72258531E-02 1.09269143E+00 1.13830695E+00 + 0 22 7.14378600E-03 7.54836211E-03 2.98680126E-01 3.15595365E-01 + 0 23 1.97316490E-02 1.96718217E-02 8.24975918E-01 8.22474553E-01 + 0 23 5.95532500E-03 6.30335292E-03 2.48990832E-01 2.63541803E-01 + 0 23 4.54993400E-03 4.21393322E-03 1.90231743E-01 1.76183624E-01 + 0 24 -1.48666990E-02 -1.48965534E-02 -6.21573425E-01 -6.22821631E-01 + 0 24 -1.16654480E-02 -1.17726588E-02 -4.87729823E-01 -4.92212282E-01 + 0 24 2.65607210E-02 2.66288341E-02 1.11049792E+00 1.11334571E+00 + 0 25 2.00906670E-02 2.04110833E-02 8.39986382E-01 8.53382918E-01 + 0 25 1.47688100E-03 1.66313368E-03 6.17480708E-02 6.95352546E-02 + 0 25 -8.31240100E-03 -8.17610680E-03 -3.47539663E-01 -3.41841233E-01 + 0 26 -3.19944400E-03 -3.41803753E-03 -1.33768052E-01 -1.42907400E-01 + 0 26 6.14351100E-03 6.19713228E-03 2.56858848E-01 2.59100742E-01 + 0 26 -1.66462830E-02 -1.65003640E-02 -6.95977442E-01 -6.89876601E-01 + 0 27 -2.53863000E-03 -1.79846027E-03 -1.06139564E-01 -7.51932294E-02 + 0 27 2.61523420E-02 2.63508393E-02 1.09342368E+00 1.10172281E+00 + 0 27 5.34609800E-03 6.06041570E-03 2.23519185E-01 2.53384651E-01 + 0 28 -1.39774410E-02 -1.45613130E-02 -5.84393743E-01 -6.08805301E-01 + 0 28 -5.71373800E-03 -6.42780419E-03 -2.38890133E-01 -2.68745084E-01 + 0 28 -2.59536100E-03 -3.14647188E-03 -1.08511474E-01 -1.31553299E-01 + 0 29 2.06325290E-02 2.00383804E-02 8.62641513E-01 8.37800292E-01 + 0 29 -2.23034250E-02 -2.21273058E-02 -9.32501308E-01 -9.25137803E-01 + 0 29 4.79241000E-03 4.03340037E-03 2.00369611E-01 1.68635585E-01 + 0 30 1.04719400E-03 1.10767599E-03 4.37829515E-02 4.63116903E-02 + 0 30 -8.18957200E-03 -7.88427169E-03 -3.42404209E-01 -3.29639670E-01 + 0 30 9.27561400E-03 9.06964557E-03 3.87811387E-01 3.79199893E-01 + 0 31 4.32571000E-03 4.57713603E-03 1.80856987E-01 1.91369054E-01 + 0 31 2.24611000E-04 2.06619971E-04 9.39093666E-03 8.63873568E-03 + 0 31 5.07335500E-03 5.32079229E-03 2.12115860E-01 2.22461159E-01 + 0 32 -4.19940400E-03 -4.41317900E-03 -1.75576160E-01 -1.84514046E-01 + 0 32 9.17287300E-03 8.40262668E-03 3.83515809E-01 3.51311979E-01 + 0 32 -1.67897470E-02 -1.74845468E-02 -7.01975640E-01 -7.31025068E-01 + 0 33 5.45957460E-02 5.49732350E-02 2.28263617E+00 2.29841890E+00 + 0 33 -6.87668600E-03 -7.90918801E-03 -2.87512734E-01 -3.30681417E-01 + 0 33 1.87334860E-02 1.78625204E-02 7.83242942E-01 7.46828063E-01 + 0 34 -3.31104570E-02 -3.28669992E-02 -1.38434095E+00 -1.37416203E+00 + 0 34 1.95654160E-02 2.04766236E-02 8.18025753E-01 8.56123141E-01 + 0 34 2.65736500E-03 2.66979514E-03 1.11103848E-01 1.11623549E-01 + 0 35 -1.91724550E-02 -1.92727055E-02 -8.01596139E-01 -8.05787589E-01 + 0 35 -1.95892100E-02 -1.96157946E-02 -8.19020574E-01 -8.20132069E-01 + 0 35 -1.94079540E-02 -1.92352461E-02 -8.11442301E-01 -8.04221421E-01 + 0 36 -3.20511400E-03 -3.93107290E-03 -1.34005114E-01 -1.64357296E-01 + 0 36 -1.24104500E-02 -1.36395482E-02 -5.18878193E-01 -5.70266520E-01 + 0 36 1.75740290E-02 1.78041472E-02 7.34766299E-01 7.44387492E-01 + 0 37 -6.35995000E-04 -4.79545337E-04 -2.65908115E-02 -2.00496854E-02 + 0 37 1.82125000E-04 1.17661961E-03 7.61460631E-03 4.91942077E-02 + 0 37 -3.01464900E-03 -2.71560376E-03 -1.26041814E-01 -1.13538798E-01 + 0 38 2.11279400E-03 1.94881353E-03 8.83354538E-02 8.14794665E-02 + 0 38 1.62847770E-02 1.64340945E-02 6.80862955E-01 6.87105886E-01 + 0 38 -1.35543420E-02 -1.37800816E-02 -5.66704067E-01 -5.76142191E-01 + 0 39 -1.09074130E-02 -1.03175537E-02 -4.56036546E-01 -4.31374659E-01 + 0 39 -9.46173100E-03 -9.21991949E-03 -3.95592898E-01 -3.85482812E-01 + 0 39 1.84730200E-03 1.42147098E-03 7.72352915E-02 5.94313900E-02 + 0 40 4.84657600E-03 4.66845451E-03 2.02634280E-01 1.95187059E-01 + 0 40 -2.79985700E-03 -3.19590403E-03 -1.17061407E-01 -1.33620047E-01 + 0 40 -8.50505700E-03 -8.68811012E-03 -3.55594568E-01 -3.63247979E-01 + 0 41 2.00386000E-03 1.32302418E-03 8.37809472E-02 5.53153510E-02 + 0 41 2.40448030E-02 2.41480825E-02 1.00530794E+00 1.00962603E+00 + 0 41 3.86057300E-03 4.76902522E-03 1.61409711E-01 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1.62393490E-02 1.59021735E-02 6.78963621E-01 6.64866388E-01 + 19 190 -1.58778180E-02 -1.61676135E-02 -6.63848089E-01 -6.75964377E-01 + 19 190 1.24715070E-02 1.24742636E-02 5.21430973E-01 5.21546228E-01 + 19 190 -1.98466590E-02 -1.97131929E-02 -8.29784461E-01 -8.24204271E-01 + 19 191 3.87777500E-03 4.38365854E-03 1.62128922E-01 1.83279802E-01 + 19 191 1.31700000E-05 -2.74099503E-04 5.50634812E-04 -1.14600401E-02 + 19 191 4.93788900E-03 5.35482795E-03 2.06452056E-01 2.23884182E-01 diff --git a/examples/nnp-dataset/H2O_RPBE-D3/nnp-dataset.log.0000 b/examples/nnp-dataset/H2O_RPBE-D3/nnp-dataset.log.0000 new file mode 100644 index 000000000..db3810d68 --- /dev/null +++ b/examples/nnp-dataset/H2O_RPBE-D3/nnp-dataset.log.0000 @@ -0,0 +1,458 @@ + +*** SETUP: MPI **************************************************************** + +Number of processors: 1 +Process 1 of 1 (rank 0): cp14.exp.univie.ac.at +******************************************************************************* + +******************************************************************************* + + NNP LIBRARY v2.0.0 + ------------------ + +Git branch : n2p2_training_size +Git revision: 0e4df6f (0e4df6fd828cf869bc711a5277efadde69bdca40) + +Number of OpenMP threads: 4 +******************************************************************************* + +*** SETUP: SETTINGS FILE ****************************************************** + +Settings file name: input.nn +Read 128 lines. +Found 72 lines with keywords. +******************************************************************************* + +*** SETUP: NORMALIZATION ****************************************************** + +Data set normalization is used. +Mean energy per atom : -2.5521343547039809E+01 +Conversion factor energy : 2.4265748255366972E+02 +Conversion factor length : 5.8038448995319847E+00 +******************************************************************************* + +*** SETUP: ELEMENT MAP ******************************************************** + +Number of element strings found: 2 +Element 0: H ( 1) +Element 1: O ( 8) +******************************************************************************* + +*** SETUP: ELEMENTS *********************************************************** + +Number of elements is consistent: 2 +Atomic energy offsets per element: +Element 0: 0.00000000E+00 +Element 1: 0.00000000E+00 +Energy offsets are automatically subtracted from reference energies. +******************************************************************************* + +*** SETUP: CUTOFF FUNCTIONS *************************************************** + +Parameter alpha for inner cutoff: 0.000000 +Inner cutoff = Symmetry function cutoff * alpha +Equal cutoff function type for all symmetry functions: +CutoffFunction::CT_TANHU (2) +f(r) = tanh^3(1 - r/rc) +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTIONS ************************************************* + +Abbreviations: +-------------- +ind .... Symmetry function index. +ec ..... Central atom element. +ty ..... Symmetry function type. +e1 ..... Neighbor 1 element. +e2 ..... Neighbor 2 element. +eta .... Gaussian width eta. +rs ..... Shift distance of Gaussian. +la ..... Angle prefactor lambda. +zeta ... Angle term exponent zeta. +rc ..... Cutoff radius. +ct ..... Cutoff type. +ca ..... Cutoff alpha. +ln ..... Line number in settings file. + +Short range atomic symmetry functions element H : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln +------------------------------------------------------------------------------- + 1 H 2 H 1.000E-03 0.000E+00 1.200E+01 2 0.00 59 + 2 H 2 O 1.000E-03 0.000E+00 1.200E+01 2 0.00 69 + 3 H 2 H 1.000E-02 0.000E+00 1.200E+01 2 0.00 60 + 4 H 2 O 1.000E-02 0.000E+00 1.200E+01 2 0.00 70 + 5 H 2 H 3.000E-02 0.000E+00 1.200E+01 2 0.00 61 + 6 H 2 O 3.000E-02 0.000E+00 1.200E+01 2 0.00 71 + 7 H 2 H 6.000E-02 0.000E+00 1.200E+01 2 0.00 62 + 8 H 2 O 6.000E-02 0.000E+00 1.200E+01 2 0.00 72 + 9 H 2 O 1.500E-01 9.000E-01 1.200E+01 2 0.00 73 + 10 H 2 H 1.500E-01 1.900E+00 1.200E+01 2 0.00 63 + 11 H 2 O 3.000E-01 9.000E-01 1.200E+01 2 0.00 74 + 12 H 2 H 3.000E-01 1.900E+00 1.200E+01 2 0.00 64 + 13 H 2 O 6.000E-01 9.000E-01 1.200E+01 2 0.00 75 + 14 H 2 H 6.000E-01 1.900E+00 1.200E+01 2 0.00 65 + 15 H 2 O 1.500E+00 9.000E-01 1.200E+01 2 0.00 76 + 16 H 2 H 1.500E+00 1.900E+00 1.200E+01 2 0.00 66 + 17 H 3 O O 1.000E-03 0.000E+00 -1 4.0 1.200E+01 2 0.00 123 + 18 H 3 O O 1.000E-03 0.000E+00 1 4.0 1.200E+01 2 0.00 122 + 19 H 3 H O 1.000E-02 0.000E+00 -1 4.0 1.200E+01 2 0.00 113 + 20 H 3 H O 1.000E-02 0.000E+00 1 4.0 1.200E+01 2 0.00 111 + 21 H 3 H O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 108 + 22 H 3 O O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 121 + 23 H 3 H O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 106 + 24 H 3 O O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 120 + 25 H 3 H O 7.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 103 + 26 H 3 H O 7.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 101 + 27 H 3 H O 2.000E-01 0.000E+00 1 1.0 1.200E+01 2 0.00 98 +------------------------------------------------------------------------------- +Short range atomic symmetry functions element O : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln +------------------------------------------------------------------------------- + 1 O 2 H 1.000E-03 0.000E+00 1.200E+01 2 0.00 78 + 2 O 2 O 1.000E-03 0.000E+00 1.200E+01 2 0.00 88 + 3 O 2 H 1.000E-02 0.000E+00 1.200E+01 2 0.00 79 + 4 O 2 O 1.000E-02 0.000E+00 1.200E+01 2 0.00 89 + 5 O 2 H 3.000E-02 0.000E+00 1.200E+01 2 0.00 80 + 6 O 2 O 3.000E-02 0.000E+00 1.200E+01 2 0.00 90 + 7 O 2 H 6.000E-02 0.000E+00 1.200E+01 2 0.00 81 + 8 O 2 O 6.000E-02 0.000E+00 1.200E+01 2 0.00 91 + 9 O 2 H 1.500E-01 9.000E-01 1.200E+01 2 0.00 82 + 10 O 2 O 1.500E-01 4.000E+00 1.200E+01 2 0.00 92 + 11 O 2 H 3.000E-01 9.000E-01 1.200E+01 2 0.00 83 + 12 O 2 O 3.000E-01 4.000E+00 1.200E+01 2 0.00 93 + 13 O 2 H 6.000E-01 9.000E-01 1.200E+01 2 0.00 84 + 14 O 2 O 6.000E-01 4.000E+00 1.200E+01 2 0.00 94 + 15 O 2 H 1.500E+00 9.000E-01 1.200E+01 2 0.00 85 + 16 O 2 O 1.500E+00 4.000E+00 1.200E+01 2 0.00 95 + 17 O 3 H O 1.000E-03 0.000E+00 -1 4.0 1.200E+01 2 0.00 118 + 18 O 3 O O 1.000E-03 0.000E+00 -1 4.0 1.200E+01 2 0.00 128 + 19 O 3 H O 1.000E-03 0.000E+00 1 4.0 1.200E+01 2 0.00 117 + 20 O 3 O O 1.000E-03 0.000E+00 1 4.0 1.200E+01 2 0.00 127 + 21 O 3 H H 1.000E-02 0.000E+00 -1 4.0 1.200E+01 2 0.00 112 + 22 O 3 H H 1.000E-02 0.000E+00 1 4.0 1.200E+01 2 0.00 110 + 23 O 3 H H 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 107 + 24 O 3 H O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 116 + 25 O 3 O O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 126 + 26 O 3 H H 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 105 + 27 O 3 H O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 115 + 28 O 3 O O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 125 + 29 O 3 H H 7.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 102 + 30 O 3 H H 7.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 100 +------------------------------------------------------------------------------- +Minimum cutoff radius for element H: 12.000000 +Minimum cutoff radius for element O: 12.000000 +Maximum cutoff radius (global) : 12.000000 +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTION GROUPS ******************************************* + +Abbreviations: +-------------- +ind .... Symmetry function group index. +ec ..... Central atom element. +ty ..... Symmetry function type. +e1 ..... Neighbor 1 element. +e2 ..... Neighbor 2 element. +eta .... Gaussian width eta. +rs ..... Shift distance of Gaussian. +la ..... Angle prefactor lambda. +zeta ... Angle term exponent zeta. +rc ..... Cutoff radius. +ct ..... Cutoff type. +ca ..... Cutoff alpha. +ln ..... Line number in settings file. +mi ..... Member index. +sfi .... Symmetry function index. +e ...... Recalculate exponential term. + +Short range atomic symmetry function groups element H : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln mi sfi e +------------------------------------------------------------------------------- + 1 H 2 H * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 58 1 1 + - - - - 1.000E-02 0.000E+00 - - - 59 2 3 + - - - - 3.000E-02 0.000E+00 - - - 60 3 5 + - - - - 6.000E-02 0.000E+00 - - - 61 4 7 + - - - - 1.500E-01 1.900E+00 - - - 62 5 10 + - - - - 3.000E-01 1.900E+00 - - - 63 6 12 + - - - - 6.000E-01 1.900E+00 - - - 64 7 14 + - - - - 1.500E+00 1.900E+00 - - - 65 8 16 + 2 H 2 O * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 68 1 2 + - - - - 1.000E-02 0.000E+00 - - - 69 2 4 + - - - - 3.000E-02 0.000E+00 - - - 70 3 6 + - - - - 6.000E-02 0.000E+00 - - - 71 4 8 + - - - - 1.500E-01 9.000E-01 - - - 72 5 9 + - - - - 3.000E-01 9.000E-01 - - - 73 6 11 + - - - - 6.000E-01 9.000E-01 - - - 74 7 13 + - - - - 1.500E+00 9.000E-01 - - - 75 8 15 + 3 H 3 H O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-02 0.000E+00 -1 4.0 - - - 112 1 19 1 + - - - - - 1.000E-02 0.000E+00 1 4.0 - - - 110 2 20 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 107 3 21 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 105 4 23 0 + - - - - - 7.000E-02 0.000E+00 -1 1.0 - - - 102 5 25 1 + - - - - - 7.000E-02 0.000E+00 1 1.0 - - - 100 6 26 0 + - - - - - 2.000E-01 0.000E+00 1 1.0 - - - 97 7 27 1 + 4 H 3 O O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-03 0.000E+00 -1 4.0 - - - 122 1 17 1 + - - - - - 1.000E-03 0.000E+00 1 4.0 - - - 121 2 18 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 120 3 22 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 119 4 24 0 +------------------------------------------------------------------------------- +Short range atomic symmetry function groups element O : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln mi sfi e +------------------------------------------------------------------------------- + 1 O 2 H * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 77 1 1 + - - - - 1.000E-02 0.000E+00 - - - 78 2 3 + - - - - 3.000E-02 0.000E+00 - - - 79 3 5 + - - - - 6.000E-02 0.000E+00 - - - 80 4 7 + - - - - 1.500E-01 9.000E-01 - - - 81 5 9 + - - - - 3.000E-01 9.000E-01 - - - 82 6 11 + - - - - 6.000E-01 9.000E-01 - - - 83 7 13 + - - - - 1.500E+00 9.000E-01 - - - 84 8 15 + 2 O 2 O * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 87 1 2 + - - - - 1.000E-02 0.000E+00 - - - 88 2 4 + - - - - 3.000E-02 0.000E+00 - - - 89 3 6 + - - - - 6.000E-02 0.000E+00 - - - 90 4 8 + - - - - 1.500E-01 4.000E+00 - - - 91 5 10 + - - - - 3.000E-01 4.000E+00 - - - 92 6 12 + - - - - 6.000E-01 4.000E+00 - - - 93 7 14 + - - - - 1.500E+00 4.000E+00 - - - 94 8 16 + 3 O 3 H H * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-02 0.000E+00 -1 4.0 - - - 111 1 21 1 + - - - - - 1.000E-02 0.000E+00 1 4.0 - - - 109 2 22 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 106 3 23 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 104 4 26 0 + - - - - - 7.000E-02 0.000E+00 -1 1.0 - - - 101 5 29 1 + - - - - - 7.000E-02 0.000E+00 1 1.0 - - - 99 6 30 0 + 4 O 3 H O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-03 0.000E+00 -1 4.0 - - - 117 1 17 1 + - - - - - 1.000E-03 0.000E+00 1 4.0 - - - 116 2 19 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 115 3 24 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 114 4 27 0 + 5 O 3 O O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-03 0.000E+00 -1 4.0 - - - 127 1 18 1 + - - - - - 1.000E-03 0.000E+00 1 4.0 - - - 126 2 20 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 125 3 25 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 124 4 28 0 +------------------------------------------------------------------------------- +******************************************************************************* + +*** SETUP: NEURAL NETWORKS **************************************************** + +Normalize neurons (all elements): 0 +------------------------------------------------------------------------------- +Atomic short range NN for element H : +Number of weights : 1325 +Number of biases : 51 +Number of connections: 1376 +Architecture 27 25 25 1 +------------------------------------------------------------------------------- + 1 G t t l + 2 G t t + 3 G t t + 4 G t t + 5 G t t + 6 G t t + 7 G t t + 8 G t t + 9 G t t + 10 G t t + 11 G t t + 12 G t t + 13 G t t + 14 G t t + 15 G t t + 16 G t t + 17 G t t + 18 G t t + 19 G t t + 20 G t t + 21 G t t + 22 G t t + 23 G t t + 24 G t t + 25 G t t + 26 G + 27 G +------------------------------------------------------------------------------- +Atomic short range NN for element O : +Number of weights : 1400 +Number of biases : 51 +Number of connections: 1451 +Architecture 30 25 25 1 +------------------------------------------------------------------------------- + 1 G t t l + 2 G t t + 3 G t t + 4 G t t + 5 G t t + 6 G t t + 7 G t t + 8 G t t + 9 G t t + 10 G t t + 11 G t t + 12 G t t + 13 G t t + 14 G t t + 15 G t t + 16 G t t + 17 G t t + 18 G t t + 19 G t t + 20 G t t + 21 G t t + 22 G t t + 23 G t t + 24 G t t + 25 G t t + 26 G + 27 G + 28 G + 29 G + 30 G +------------------------------------------------------------------------------- +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTION SCALING ****************************************** + +Equal scaling type for all symmetry functions: +Scaling type::ST_SCALECENTER (3) +Gs = Smin + (Smax - Smin) * (G - Gmean) / (Gmax - Gmin) +Smin = 0.000000 +Smax = 1.000000 +Symmetry function scaling statistics from file: scaling.data +------------------------------------------------------------------------------- + +Abbreviations: +-------------- +ind ..... Symmetry function index. +min ..... Minimum symmetry function value. +max ..... Maximum symmetry function value. +mean .... Mean symmetry function value. +sigma ... Standard deviation of symmetry function values. +sf ...... Scaling factor for derivatives. +Smin .... Desired minimum scaled symmetry function value. +Smax .... Desired maximum scaled symmetry function value. +t ....... Scaling type. + +Scaling data for symmetry functions element H : +------------------------------------------------------------------------------- + ind min max mean sigma sf Smin Smax t +------------------------------------------------------------------------------- + 1 1.09E+00 9.62E+00 2.27E+00 6.79E-01 1.17E-01 0.00 1.00 3 + 2 7.33E-01 5.00E+00 1.33E+00 3.39E-01 2.34E-01 0.00 1.00 3 + 3 7.60E-01 7.14E+00 1.65E+00 5.08E-01 1.57E-01 0.00 1.00 3 + 4 5.48E-01 3.77E+00 1.02E+00 2.54E-01 3.11E-01 0.00 1.00 3 + 5 4.01E-01 4.15E+00 9.09E-01 2.98E-01 2.67E-01 0.00 1.00 3 + 6 3.62E-01 2.27E+00 6.49E-01 1.48E-01 5.25E-01 0.00 1.00 3 + 7 1.89E-01 2.23E+00 4.57E-01 1.60E-01 4.90E-01 0.00 1.00 3 + 8 2.67E-01 1.32E+00 4.24E-01 8.05E-02 9.49E-01 0.00 1.00 3 + 9 2.45E-01 9.48E-01 3.62E-01 5.30E-02 1.42E+00 0.00 1.00 3 + 10 2.22E-01 2.76E+00 5.39E-01 2.01E-01 3.94E-01 0.00 1.00 3 + 11 1.47E-01 5.56E-01 2.68E-01 2.62E-02 2.45E+00 0.00 1.00 3 + 12 9.91E-02 1.73E+00 2.96E-01 1.16E-01 6.14E-01 0.00 1.00 3 + 13 6.51E-02 3.45E-01 1.85E-01 1.97E-02 3.57E+00 0.00 1.00 3 + 14 3.17E-02 9.13E-01 1.50E-01 5.35E-02 1.13E+00 0.00 1.00 3 + 15 2.92E-03 2.65E-01 7.65E-02 1.88E-02 3.82E+00 0.00 1.00 3 + 16 3.21E-04 2.87E-01 4.58E-02 2.33E-02 3.49E+00 0.00 1.00 3 + 17 2.47E-04 1.38E-01 1.77E-02 9.75E-03 7.23E+00 0.00 1.00 3 + 18 5.10E-03 5.83E-01 2.39E-02 3.78E-02 1.73E+00 0.00 1.00 3 + 19 3.23E-04 2.16E-01 1.71E-02 1.40E-02 4.63E+00 0.00 1.00 3 + 20 4.96E-02 1.69E+00 1.45E-01 1.10E-01 6.11E-01 0.00 1.00 3 + 21 3.41E-03 3.16E-01 1.84E-02 2.01E-02 3.20E+00 0.00 1.00 3 + 22 1.31E-04 1.03E-01 6.37E-03 6.61E-03 9.76E+00 0.00 1.00 3 + 23 3.38E-02 9.16E-01 8.13E-02 5.79E-02 1.13E+00 0.00 1.00 3 + 24 4.17E-04 1.58E-01 4.66E-03 9.86E-03 6.35E+00 0.00 1.00 3 + 25 7.35E-04 5.92E-02 3.70E-03 3.31E-03 1.71E+01 0.00 1.00 3 + 26 8.98E-03 1.94E-01 2.41E-02 1.10E-02 5.40E+00 0.00 1.00 3 + 27 2.12E-04 8.78E-03 2.06E-03 5.88E-04 1.17E+02 0.00 1.00 3 +------------------------------------------------------------------------------- +Scaling data for symmetry functions element O : +------------------------------------------------------------------------------- + ind min max mean sigma sf Smin Smax t +------------------------------------------------------------------------------- + 1 1.51E+00 1.00E+01 2.65E+00 6.78E-01 1.18E-01 0.00 1.00 3 + 2 4.44E-01 4.62E+00 9.66E-01 3.37E-01 2.39E-01 0.00 1.00 3 + 3 1.19E+00 7.53E+00 2.03E+00 5.06E-01 1.58E-01 0.00 1.00 3 + 4 2.76E-01 3.39E+00 6.59E-01 2.50E-01 3.21E-01 0.00 1.00 3 + 5 8.06E-01 4.54E+00 1.30E+00 2.94E-01 2.68E-01 0.00 1.00 3 + 6 1.05E-01 1.89E+00 3.07E-01 1.42E-01 5.60E-01 0.00 1.00 3 + 7 5.69E-01 2.62E+00 8.48E-01 1.57E-01 4.89E-01 0.00 1.00 3 + 8 2.33E-02 9.36E-01 1.11E-01 6.98E-02 1.10E+00 0.00 1.00 3 + 9 5.14E-01 1.85E+00 7.25E-01 9.80E-02 7.46E-01 0.00 1.00 3 + 10 1.11E-01 2.91E+00 4.75E-01 2.34E-01 3.57E-01 0.00 1.00 3 + 11 3.53E-01 1.07E+00 5.35E-01 4.52E-02 1.39E+00 0.00 1.00 3 + 12 3.04E-02 2.53E+00 3.17E-01 2.10E-01 4.00E-01 0.00 1.00 3 + 13 1.60E-01 6.63E-01 3.70E-01 3.08E-02 1.99E+00 0.00 1.00 3 + 14 2.78E-03 2.30E+00 1.77E-01 1.86E-01 4.35E-01 0.00 1.00 3 + 15 9.56E-03 3.91E-01 1.53E-01 2.79E-02 2.62E+00 0.00 1.00 3 + 16 3.75E-06 2.04E+00 5.41E-02 1.43E-01 4.91E-01 0.00 1.00 3 + 17 2.47E-03 3.43E-01 1.67E-02 2.19E-02 2.93E+00 0.00 1.00 3 + 18 1.74E-05 5.63E-02 9.55E-04 3.36E-03 1.78E+01 0.00 1.00 3 + 19 5.48E-02 3.02E+00 2.04E-01 2.01E-01 3.37E-01 0.00 1.00 3 + 20 1.38E-03 4.99E-01 1.28E-02 3.18E-02 2.01E+00 0.00 1.00 3 + 21 6.69E-03 2.67E-01 3.09E-02 1.71E-02 3.84E+00 0.00 1.00 3 + 22 1.70E-02 1.42E+00 7.63E-02 9.29E-02 7.14E-01 0.00 1.00 3 + 23 1.98E-02 4.08E-01 4.88E-02 2.55E-02 2.58E+00 0.00 1.00 3 + 24 5.28E-04 2.33E-01 7.21E-03 1.45E-02 4.30E+00 0.00 1.00 3 + 25 1.11E-05 3.53E-02 4.25E-04 2.05E-03 2.83E+01 0.00 1.00 3 + 26 1.60E-02 8.22E-01 5.08E-02 5.28E-02 1.24E+00 0.00 1.00 3 + 27 3.99E-03 7.86E-01 3.69E-02 5.05E-02 1.28E+00 0.00 1.00 3 + 28 4.05E-05 9.84E-02 1.21E-03 5.79E-03 1.02E+01 0.00 1.00 3 + 29 6.04E-03 9.93E-02 1.62E-02 5.52E-03 1.07E+01 0.00 1.00 3 + 30 2.96E-03 1.55E-01 1.16E-02 8.94E-03 6.59E+00 0.00 1.00 3 +------------------------------------------------------------------------------- +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTION STATISTICS *************************************** + +Equal symmetry function statistics for all elements. +Collect min/max/mean/sigma : 0 +Collect extrapolation warnings : 0 +Write extrapolation warnings immediately to stderr: 1 +Halt on any extrapolation warning : 0 +******************************************************************************* + +*** SETUP: NEURAL NETWORK WEIGHTS ********************************************* + +Weight file name format: weights.%03zu.data +Weight file for element H: weights.001.data +Weight file for element O: weights.008.data +******************************************************************************* + +*** STRUCTURE DISTRIBUTION **************************************************** + +Reading configurations from data file: input.data. +Total number of structures: 20 +Number of structures per processor: 20 +Distributed 20 structures, 0 bytes (0.00 MiB) transferred. +Number of local structures: 20 +******************************************************************************* + +*** DATA SET PREDICTION ******************************************************* + +Energies and forces are predicted. +Energy and force comparison in files: + - energy.comp + - forces.comp +Predicted data set in "output.data" +Root mean squared errors for energies and forces: +ENERGY S 2.86565E-05 0.00000E+00 6.95372E-03 0.00000E+00 +FORCES S 7.41795E-04 0.00000E+00 3.10143E-02 0.00000E+00 +******************************************************************************* + +*** SENSITIVITY ANALYSIS ****************************************************** + +Writing sensitivity analysis data to files: + - sensitivity.001.out + - sensitivity.008.out +******************************************************************************* diff --git a/examples/nnp-dataset/H2O_RPBE-D3/output.data b/examples/nnp-dataset/H2O_RPBE-D3/output.data new file mode 100644 index 000000000..61eb51802 --- /dev/null +++ b/examples/nnp-dataset/H2O_RPBE-D3/output.data @@ -0,0 +1,2224 @@ +begin +comment written by RuNNerDecom +lattice 2.4623508000000001E+01 0.0000000000000000E+00 0.0000000000000000E+00 +lattice 0.0000000000000000E+00 2.4623508000000001E+01 0.0000000000000000E+00 +lattice 0.0000000000000000E+00 0.0000000000000000E+00 2.4623508000000001E+01 +atom 3.7352908379999992E+00 2.3278411569999995E+00 3.5013048559999991E+00 O -2.2638936000000001E-01 0.0000000000000000E+00 -5.5201808868798902E-03 -2.5574755788383328E-03 -2.7275923232827036E-02 +atom 5.2465243199999989E+00 3.3930613009999986E+00 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+charge 0.0000000000000000E+00 +end diff --git a/examples/nnp-dataset/H2O_RPBE-D3/sensitivity.001.out b/examples/nnp-dataset/H2O_RPBE-D3/sensitivity.001.out new file mode 100644 index 000000000..d00177b29 --- /dev/null +++ b/examples/nnp-dataset/H2O_RPBE-D3/sensitivity.001.out @@ -0,0 +1,43 @@ +################################################################################ +# Sensitivity analysis for element H. +################################################################################ +# Col Name Description +################################################################################ +# 1 index Symmetry function index. +# 2 sens_msa_norm Mean square average sensitivity (normalized, sum = 100%). +# 3 sens_max_norm Maximum sensitivity (normalized, sum = 100%). +# 4 sens_msa_phys Mean square average sensitivity (physical energy units). +# 5 sens_max_phys Maximum sensitivity (physical energy units). +# 6 sens_msa_int Mean square average sensitivity (internal units). +# 7 sens_max_int Maximum sensitivity (internal units). +################################################################################################################ +# 1 2 3 4 5 6 7 +# index sens_msa_norm sens_max_norm sens_msa_phys sens_max_phys sens_msa_int sens_max_int +################################################################################################################ + 1 1.97170552E+00 3.02857692E+00 1.44013644E-01 4.29300706E-01 3.49459883E+01 1.04173028E+02 + 2 2.00719606E+00 3.25187967E+00 1.46605878E-01 4.60953866E-01 3.55750133E+01 1.11853905E+02 + 3 3.61027972E+00 5.52864200E+00 2.63695331E-01 7.83684871E-01 6.39876452E+01 1.90166998E+02 + 4 2.47220894E+00 4.62013389E+00 1.80570484E-01 6.54903869E-01 4.38167790E+01 1.58917324E+02 + 5 5.18851575E+00 6.63378652E+00 3.78969909E-01 9.40339081E-01 9.19598842E+01 2.28180314E+02 + 6 5.34656738E+00 5.80432546E+00 3.90514022E-01 8.22762995E-01 9.47611494E+01 1.99649597E+02 + 7 1.63881962E+00 3.97255475E+00 1.19699612E-01 5.63109542E-01 2.90460064E+01 1.36642744E+02 + 8 6.11078301E+00 5.88642162E+00 4.46332438E-01 8.34400124E-01 1.08305906E+02 2.02473433E+02 + 9 2.80797933E+00 3.04080020E+00 2.05095200E-01 4.31033356E-01 4.97678848E+01 1.04593469E+02 + 10 9.40951931E-01 2.04243583E+00 6.87272594E-02 2.89515230E-01 1.66771838E+01 7.02530368E+01 + 11 1.63711285E+00 1.65489411E+00 1.19574949E-01 2.34581200E-01 2.90157562E+01 5.69228835E+01 + 12 4.26603416E-01 8.26070923E-01 3.11591726E-02 1.17095534E-01 7.56100638E+00 2.84141074E+01 + 13 1.12319823E+00 9.51965296E-01 8.20385543E-02 1.34941058E-01 1.99072691E+01 3.27444574E+01 + 14 6.33097437E-01 8.32408066E-01 4.62415245E-02 1.17993824E-01 1.12208519E+01 2.86320842E+01 + 15 4.83942340E-01 1.40593209E+00 3.53472156E-02 1.99290840E-01 8.57726635E+00 4.83594135E+01 + 16 2.34792494E-01 6.96284037E-01 1.71492763E-02 9.86982456E-02 4.16140022E+00 2.39498678E+01 + 17 3.31460159E-01 6.96370054E-01 2.42098960E-02 9.87104386E-02 5.87471241E+00 2.39528265E+01 + 18 8.80621397E+00 5.66005131E+00 6.43207088E-01 8.02312138E-01 1.56079013E+02 1.94687044E+02 + 19 2.06707567E-01 4.74240485E-01 1.50979493E-02 6.72235774E-02 3.66363038E+00 1.63123041E+01 + 20 9.43776502E+00 7.49266845E+00 6.89335665E-01 1.06208557E+00 1.67272457E+02 2.57723012E+02 + 21 7.84750991E+00 7.25796961E+00 5.73183211E-01 1.02881702E+00 1.39087195E+02 2.49650148E+02 + 22 3.29975887E+00 4.24187543E+00 2.41014845E-01 6.01285741E-01 5.84840556E+01 1.45906484E+02 + 23 1.26930715E+01 9.62183105E+00 9.27103702E-01 1.36389432E+00 2.24968651E+02 3.30959163E+02 + 24 5.50627349E+00 3.69088976E+00 4.02178979E-01 5.23183535E-01 9.75917385E+01 1.26954399E+02 + 25 6.42521799E+00 4.26905940E+00 4.69298812E-01 6.05139068E-01 1.13878868E+02 1.46841523E+02 + 26 7.70221206E+00 5.10173610E+00 5.62570636E-01 7.23170970E-01 1.36511974E+02 1.75482847E+02 + 27 1.11005541E+00 1.31619699E+00 8.10786011E-02 1.86570891E-01 1.96743292E+01 4.52728228E+01 diff --git a/examples/nnp-dataset/H2O_RPBE-D3/sensitivity.008.out b/examples/nnp-dataset/H2O_RPBE-D3/sensitivity.008.out new file mode 100644 index 000000000..a5faa9eeb --- /dev/null +++ b/examples/nnp-dataset/H2O_RPBE-D3/sensitivity.008.out @@ -0,0 +1,46 @@ +################################################################################ +# Sensitivity analysis for element O. +################################################################################ +# Col Name Description +################################################################################ +# 1 index Symmetry function index. +# 2 sens_msa_norm Mean square average sensitivity (normalized, sum = 100%). +# 3 sens_max_norm Maximum sensitivity (normalized, sum = 100%). +# 4 sens_msa_phys Mean square average sensitivity (physical energy units). +# 5 sens_max_phys Maximum sensitivity (physical energy units). +# 6 sens_msa_int Mean square average sensitivity (internal units). +# 7 sens_max_int Maximum sensitivity (internal units). +################################################################################################################ +# 1 2 3 4 5 6 7 +# index sens_msa_norm sens_max_norm sens_msa_phys sens_max_phys sens_msa_int sens_max_int +################################################################################################################ + 1 9.11698888E-01 1.25370328E+00 1.40121468E-01 3.45865883E-01 3.40015227E+01 8.39269446E+01 + 2 2.53192343E+00 4.42258713E+00 3.89138161E-01 1.22008295E+00 9.44272866E+01 2.96062258E+02 + 3 9.19746784E-01 1.53168651E+00 1.41358371E-01 4.22554614E-01 3.43016665E+01 1.02536039E+02 + 4 5.36305857E+00 8.27788182E+00 8.24262978E-01 2.28366388E+00 2.00013579E+02 5.54148129E+02 + 5 2.81855242E+00 3.67673699E+00 4.33190946E-01 1.01432125E+00 1.05117024E+02 2.46132642E+02 + 6 4.00681946E+00 6.36659805E+00 6.15818922E-01 1.75638773E+00 1.49433069E+02 4.26200624E+02 + 7 2.27278697E+00 2.58519031E+00 3.49310777E-01 7.13190386E-01 8.47628737E+01 1.73060984E+02 + 8 4.59989389E+00 6.58409353E+00 7.06970134E-01 1.81638938E+00 1.71551593E+02 4.40760473E+02 + 9 2.40804685E+00 2.08875161E+00 3.70099234E-01 5.76235166E-01 8.98073484E+01 1.39827775E+02 + 10 7.42509007E-01 1.41975265E+00 1.14118218E-01 3.91674817E-01 2.76916394E+01 9.50428250E+01 + 11 2.48569711E-01 5.63032040E-01 3.82033513E-02 1.55326684E-01 9.27032904E+00 3.76911821E+01 + 12 3.94109241E-01 5.75812932E-01 6.05717153E-02 1.58852618E-01 1.46981799E+01 3.85467763E+01 + 13 1.71929585E-01 2.94230340E-01 2.64243230E-02 8.11709102E-02 6.41205970E+00 1.96967287E+01 + 14 9.23585481E-02 3.30866586E-01 1.41948351E-02 9.12779486E-02 3.44448295E+00 2.21492772E+01 + 15 6.91535987E-01 5.61023153E-01 1.06284036E-01 1.54772482E-01 2.57906167E+01 3.75567008E+01 + 16 4.31870986E-02 8.99199802E-02 6.63754200E-03 2.48067097E-02 1.61064923E+00 6.01953372E+00 + 17 5.69708617E+00 4.26935823E+00 8.75600583E-01 1.17781087E+00 2.12471033E+02 2.85804620E+02 + 18 8.49900871E-01 1.08692545E+00 1.30623564E-01 2.99855983E-01 3.16967851E+01 7.27622980E+01 + 19 7.08623662E+00 6.15405192E+00 1.08910287E+00 1.69775148E+00 2.64278962E+02 4.11972100E+02 + 20 5.88298264E+00 4.83563424E+00 9.04171514E-01 1.33403249E+00 2.19403983E+02 3.23712965E+02 + 21 2.30587503E+00 1.67595287E+00 3.54396170E-01 4.62354153E-01 8.59968826E+01 1.12193695E+02 + 22 1.67675691E+00 1.52471023E+00 2.57705305E-01 4.20630030E-01 6.25341206E+01 1.02069024E+02 + 23 7.38305459E+00 5.25579576E+00 1.13472163E+00 1.44994471E+00 2.75348694E+02 3.51839933E+02 + 24 9.26876276E+00 8.20090766E+00 1.42454122E+00 2.26242861E+00 3.45675586E+02 5.48995230E+02 + 25 8.41294836E+00 5.77127554E+00 1.29300879E+00 1.59215289E+00 3.13758258E+02 3.86347813E+02 + 26 1.28350104E+00 1.67511234E+00 1.97264746E-01 4.62122273E-01 4.78677667E+01 1.12137427E+02 + 27 1.09586030E+01 9.50168287E+00 1.68425734E+00 2.62128048E+00 4.08697645E+02 6.36073322E+02 + 28 4.49103174E+00 4.74404592E+00 6.90238817E-01 1.30876552E+00 1.67491614E+02 3.17581747E+02 + 29 3.82582742E+00 2.82248544E+00 5.88001765E-01 7.78654275E-01 1.42683028E+02 1.88946286E+02 + 30 2.66070642E+00 1.86019462E+00 4.08931167E-01 5.13181917E-01 9.92302076E+01 1.24527432E+02 diff --git a/examples/nnp-predict/H2O_RPBE-D3/energy.out b/examples/nnp-predict/H2O_RPBE-D3/energy.out new file mode 100644 index 000000000..b27ebe444 --- /dev/null +++ b/examples/nnp-predict/H2O_RPBE-D3/energy.out @@ -0,0 +1,14 @@ +################################################################################ +# Energy comparison. +################################################################################ +# Col Name Description +################################################################################ +# 1 Ennp Potential energy predicted by NNP. +# 2 Eref Reference potential energy. +# 3 Ediff Difference between reference and NNP prediction. +# 4 E_offset Sum of atomic offset energies (included in column Ennp). +################################################################################ +# 1 2 3 4 +# Ennp Eref Ediff E_offset +################################################################### + -2.75645473E+04 0.00000000E+00 2.75645473E+04 0.00000000E+00 diff --git a/examples/nnp-predict/H2O_RPBE-D3/nnatoms.out b/examples/nnp-predict/H2O_RPBE-D3/nnatoms.out new file mode 100644 index 000000000..4c2b32be3 --- /dev/null +++ b/examples/nnp-predict/H2O_RPBE-D3/nnatoms.out @@ -0,0 +1,1093 @@ +################################################################################ +# Energy contributions calculated from NNP. +################################################################################ +# Col Name Description +################################################################################ +# 1 index Atom index. +# 2 e Element of atom. +# 3 charge Atomic charge (not used). +# 4 Ennp_atom Atomic energy contribution (physical units, no mean or offset energy added). +################################################################################ +# 1 2 3 4 +# index e charge Ennp_atom +############################################### + 0 O 0.00000000E+00 1.50471509E-02 + 1 H 0.00000000E+00 -5.17782251E-03 + 2 H 0.00000000E+00 -5.37306961E-03 + 3 O 0.00000000E+00 1.18646850E-02 + 4 H 0.00000000E+00 -9.02703820E-03 + 5 H 0.00000000E+00 -9.26819320E-03 + 6 O 0.00000000E+00 3.52556039E-03 + 7 H 0.00000000E+00 -5.24560125E-03 + 8 H 0.00000000E+00 -2.91197824E-03 + 9 O 0.00000000E+00 -8.37043427E-03 + 10 H 0.00000000E+00 1.91877983E-03 + 11 H 0.00000000E+00 -7.71241442E-03 + 12 O 0.00000000E+00 1.46716398E-02 + 13 H 0.00000000E+00 -9.08290324E-03 + 14 H 0.00000000E+00 -1.04491522E-02 + 15 O 0.00000000E+00 2.22678746E-02 + 16 H 0.00000000E+00 -8.57154746E-03 + 17 H 0.00000000E+00 -6.65274797E-03 + 18 O 0.00000000E+00 1.93061803E-02 + 19 H 0.00000000E+00 -5.55916060E-03 + 20 H 0.00000000E+00 -8.34916720E-03 + 21 O 0.00000000E+00 1.15023765E-03 + 22 H 0.00000000E+00 -1.22158771E-02 + 23 H 0.00000000E+00 -8.68404873E-03 + 24 O 0.00000000E+00 2.16373977E-02 + 25 H 0.00000000E+00 -1.30944791E-02 + 26 H 0.00000000E+00 -8.27768306E-03 + 27 O 0.00000000E+00 -1.33442345E-03 + 28 H 0.00000000E+00 -2.91358551E-03 + 29 H 0.00000000E+00 -2.76300265E-03 + 30 O 0.00000000E+00 8.46273704E-03 + 31 H 0.00000000E+00 -1.04085509E-02 + 32 H 0.00000000E+00 -1.07436599E-02 + 33 O 0.00000000E+00 9.84645396E-03 + 34 H 0.00000000E+00 -8.18541146E-03 + 35 H 0.00000000E+00 -2.22335990E-03 + 36 O 0.00000000E+00 2.57558794E-02 + 37 H 0.00000000E+00 -1.21430342E-02 + 38 H 0.00000000E+00 -8.07670981E-03 + 39 O 0.00000000E+00 8.17388516E-03 + 40 H 0.00000000E+00 -3.66687431E-03 + 41 H 0.00000000E+00 -9.86761456E-03 + 42 O 0.00000000E+00 1.25296515E-02 + 43 H 0.00000000E+00 -3.91009503E-03 + 44 H 0.00000000E+00 -8.29966491E-03 + 45 O 0.00000000E+00 2.32285841E-03 + 46 H 0.00000000E+00 -7.11188194E-03 + 47 H 0.00000000E+00 -4.19306516E-03 + 48 O 0.00000000E+00 1.01673110E-03 + 49 H 0.00000000E+00 -4.77455855E-03 + 50 H 0.00000000E+00 -1.00982715E-02 + 51 O 0.00000000E+00 1.17042818E-02 + 52 H 0.00000000E+00 -9.24963238E-03 + 53 H 0.00000000E+00 -6.93202849E-03 + 54 O 0.00000000E+00 5.47714860E-03 + 55 H 0.00000000E+00 1.97561308E-03 + 56 H 0.00000000E+00 -6.98096413E-03 + 57 O 0.00000000E+00 9.69341131E-03 + 58 H 0.00000000E+00 -9.10454408E-03 + 59 H 0.00000000E+00 -4.11838897E-03 + 60 O 0.00000000E+00 2.43062070E-03 + 61 H 0.00000000E+00 -2.25402352E-03 + 62 H 0.00000000E+00 -9.93883961E-03 + 63 O 0.00000000E+00 1.02032698E-02 + 64 H 0.00000000E+00 -9.02426074E-03 + 65 H 0.00000000E+00 -1.05302338E-02 + 66 O 0.00000000E+00 1.30041773E-02 + 67 H 0.00000000E+00 -7.73279693E-03 + 68 H 0.00000000E+00 -7.02685430E-03 + 69 O 0.00000000E+00 5.58800202E-03 + 70 H 0.00000000E+00 -3.36383769E-03 + 71 H 0.00000000E+00 -4.21825204E-03 + 72 O 0.00000000E+00 8.45121790E-03 + 73 H 0.00000000E+00 -4.60074837E-03 + 74 H 0.00000000E+00 -6.43427204E-03 + 75 O 0.00000000E+00 1.56519281E-02 + 76 H 0.00000000E+00 -8.86578947E-03 + 77 H 0.00000000E+00 -6.41347359E-03 + 78 O 0.00000000E+00 -2.62988740E-03 + 79 H 0.00000000E+00 -6.88667339E-03 + 80 H 0.00000000E+00 -6.33996573E-03 + 81 O 0.00000000E+00 7.58771876E-03 + 82 H 0.00000000E+00 -5.83588272E-03 + 83 H 0.00000000E+00 -7.28829104E-03 + 84 O 0.00000000E+00 6.43319179E-03 + 85 H 0.00000000E+00 -6.37051392E-03 + 86 H 0.00000000E+00 -7.24288101E-03 + 87 O 0.00000000E+00 -1.01605012E-03 + 88 H 0.00000000E+00 -5.68488141E-03 + 89 H 0.00000000E+00 -2.73873240E-03 + 90 O 0.00000000E+00 1.17278861E-02 + 91 H 0.00000000E+00 -8.36332936E-03 + 92 H 0.00000000E+00 -7.22675965E-03 + 93 O 0.00000000E+00 1.64066023E-02 + 94 H 0.00000000E+00 -8.08197855E-03 + 95 H 0.00000000E+00 -8.16370023E-03 + 96 O 0.00000000E+00 7.25501100E-03 + 97 H 0.00000000E+00 -2.56547678E-03 + 98 H 0.00000000E+00 -5.51504135E-03 + 99 O 0.00000000E+00 4.61645803E-03 + 100 H 0.00000000E+00 -8.68119253E-03 + 101 H 0.00000000E+00 -1.10467913E-02 + 102 O 0.00000000E+00 2.16062944E-03 + 103 H 0.00000000E+00 -1.05942294E-02 + 104 H 0.00000000E+00 -4.67872600E-03 + 105 O 0.00000000E+00 1.54824433E-02 + 106 H 0.00000000E+00 -9.39812210E-03 + 107 H 0.00000000E+00 -1.35804033E-02 + 108 O 0.00000000E+00 8.28992221E-03 + 109 H 0.00000000E+00 -1.03339093E-02 + 110 H 0.00000000E+00 -5.87781123E-03 + 111 O 0.00000000E+00 1.01508188E-02 + 112 H 0.00000000E+00 -5.34137860E-03 + 113 H 0.00000000E+00 -7.50668942E-03 + 114 O 0.00000000E+00 1.13877773E-02 + 115 H 0.00000000E+00 -8.95222225E-03 + 116 H 0.00000000E+00 -5.02885798E-03 + 117 O 0.00000000E+00 1.78082318E-02 + 118 H 0.00000000E+00 -9.78169067E-03 + 119 H 0.00000000E+00 -7.64563673E-03 + 120 O 0.00000000E+00 2.32129048E-02 + 121 H 0.00000000E+00 -9.40454694E-03 + 122 H 0.00000000E+00 -8.00672788E-03 + 123 O 0.00000000E+00 1.16189200E-02 + 124 H 0.00000000E+00 -9.30296833E-03 + 125 H 0.00000000E+00 -8.79829773E-03 + 126 O 0.00000000E+00 6.61548542E-03 + 127 H 0.00000000E+00 -5.31140562E-03 + 128 H 0.00000000E+00 -9.60991237E-03 + 129 O 0.00000000E+00 1.81271359E-02 + 130 H 0.00000000E+00 -1.04952544E-02 + 131 H 0.00000000E+00 -8.75181926E-03 + 132 O 0.00000000E+00 1.81497362E-03 + 133 H 0.00000000E+00 -3.00026254E-03 + 134 H 0.00000000E+00 -7.91099421E-03 + 135 O 0.00000000E+00 8.64094759E-03 + 136 H 0.00000000E+00 -6.51077871E-03 + 137 H 0.00000000E+00 -1.11251990E-02 + 138 O 0.00000000E+00 1.49960177E-02 + 139 H 0.00000000E+00 -7.13828949E-03 + 140 H 0.00000000E+00 -7.48110100E-03 + 141 O 0.00000000E+00 3.94964529E-03 + 142 H 0.00000000E+00 -5.25721968E-03 + 143 H 0.00000000E+00 -1.21218535E-02 + 144 O 0.00000000E+00 -3.49465395E-04 + 145 H 0.00000000E+00 -3.09189312E-03 + 146 H 0.00000000E+00 -3.76321749E-03 + 147 O 0.00000000E+00 4.42034208E-03 + 148 H 0.00000000E+00 -3.01300130E-03 + 149 H 0.00000000E+00 -9.18440135E-03 + 150 O 0.00000000E+00 1.34342708E-02 + 151 H 0.00000000E+00 -4.02495007E-03 + 152 H 0.00000000E+00 -5.36068253E-03 + 153 O 0.00000000E+00 1.84718630E-03 + 154 H 0.00000000E+00 -3.38586190E-04 + 155 H 0.00000000E+00 -9.95562858E-03 + 156 O 0.00000000E+00 7.83507172E-03 + 157 H 0.00000000E+00 -5.39111526E-03 + 158 H 0.00000000E+00 -2.50134114E-03 + 159 O 0.00000000E+00 6.36463848E-03 + 160 H 0.00000000E+00 -1.00112664E-02 + 161 H 0.00000000E+00 -6.32067602E-03 + 162 O 0.00000000E+00 -1.32681922E-03 + 163 H 0.00000000E+00 -8.06133148E-03 + 164 H 0.00000000E+00 -4.85116326E-03 + 165 O 0.00000000E+00 1.35496997E-02 + 166 H 0.00000000E+00 -7.86700863E-03 + 167 H 0.00000000E+00 -1.03461351E-02 + 168 O 0.00000000E+00 1.63581556E-02 + 169 H 0.00000000E+00 -5.48809106E-03 + 170 H 0.00000000E+00 -4.98749129E-03 + 171 O 0.00000000E+00 -2.88792152E-03 + 172 H 0.00000000E+00 -7.65919588E-03 + 173 H 0.00000000E+00 -3.21212770E-03 + 174 O 0.00000000E+00 7.86131602E-03 + 175 H 0.00000000E+00 -9.95971951E-03 + 176 H 0.00000000E+00 -3.59886737E-03 + 177 O 0.00000000E+00 1.32266170E-02 + 178 H 0.00000000E+00 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0.00000000E+00 -6.52226917E-03 + 1076 H 0.00000000E+00 -5.02503855E-03 + 1077 O 0.00000000E+00 3.43106405E-03 + 1078 H 0.00000000E+00 -1.00048070E-02 + 1079 H 0.00000000E+00 -1.54567519E-03 diff --git a/examples/nnp-predict/H2O_RPBE-D3/nnforces.out b/examples/nnp-predict/H2O_RPBE-D3/nnforces.out new file mode 100644 index 000000000..863d7c8a2 --- /dev/null +++ b/examples/nnp-predict/H2O_RPBE-D3/nnforces.out @@ -0,0 +1,1098 @@ +################################################################################ +# Atomic force comparison (ordered by atom index). +################################################################################ +# Col Name Description +################################################################################ +# 1 fx Force in x direction. +# 2 fy Force in y direction. +# 3 fz Force in z direction. +# 4 fxRef Reference force in x direction. +# 5 fyRef Reference force in y direction. +# 6 fzRef Reference force in z direction. +# 7 fxDiff Difference between reference and NNP force in x dir. +# 8 fyDiff Difference between reference and NNP force in y dir. +# 9 fzDiff Difference between reference and NNP force in z dir. +######################################################################################################################################################## +# 1 2 3 4 5 6 7 8 9 +# fx fy fz fxRef fyRef fzRef fxDiff fyDiff fzDiff +######################################################################################################################################################## + -2.89660301E-02 2.72095998E-03 3.97370214E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 2.89660301E-02 -2.72095998E-03 -3.97370214E-03 + 7.98774238E-03 3.32976047E-03 -1.01638330E-02 0.00000000E+00 0.00000000E+00 0.00000000E+00 -7.98774238E-03 -3.32976047E-03 1.01638330E-02 + 1.18069028E-02 -9.49625097E-03 6.63343658E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 -1.18069028E-02 9.49625097E-03 -6.63343658E-03 + 1.58114603E-03 1.50108414E-03 -1.91253456E-02 0.00000000E+00 0.00000000E+00 0.00000000E+00 -1.58114603E-03 -1.50108414E-03 1.91253456E-02 + -5.59046661E-03 8.94407619E-03 -2.23070103E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 5.59046661E-03 -8.94407619E-03 2.23070103E-03 + 3.01737375E-04 -1.01259575E-02 1.38573559E-02 0.00000000E+00 0.00000000E+00 0.00000000E+00 -3.01737375E-04 1.01259575E-02 -1.38573559E-02 + -7.80967126E-04 -1.67088140E-03 -4.69678066E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 7.80967126E-04 1.67088140E-03 4.69678066E-03 + 2.24274610E-03 -5.08242407E-03 2.83147010E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 -2.24274610E-03 5.08242407E-03 -2.83147010E-03 + 1.43702093E-03 6.00233905E-03 6.12526798E-04 0.00000000E+00 0.00000000E+00 0.00000000E+00 -1.43702093E-03 -6.00233905E-03 -6.12526798E-04 + -2.34704279E-03 -3.63775225E-02 -2.51160718E-02 0.00000000E+00 0.00000000E+00 0.00000000E+00 2.34704279E-03 3.63775225E-02 2.51160718E-02 + -5.40416012E-03 2.60317761E-02 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7.56808908E-03 + 1.06857392E-02 1.31329226E-02 -2.97260165E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 -1.06857392E-02 -1.31329226E-02 2.97260165E-03 + -3.24192497E-03 3.64764573E-04 3.18206780E-05 0.00000000E+00 0.00000000E+00 0.00000000E+00 3.24192497E-03 -3.64764573E-04 -3.18206780E-05 + -5.13992763E-03 -1.57758899E-02 3.75299362E-03 0.00000000E+00 0.00000000E+00 0.00000000E+00 5.13992763E-03 1.57758899E-02 -3.75299362E-03 diff --git a/examples/nnp-predict/H2O_RPBE-D3/nnp-predict.log b/examples/nnp-predict/H2O_RPBE-D3/nnp-predict.log new file mode 100644 index 000000000..df3f2e6a1 --- /dev/null +++ b/examples/nnp-predict/H2O_RPBE-D3/nnp-predict.log @@ -0,0 +1,1523 @@ + +******************************************************************************* + + NNP LIBRARY v2.0.0 + ------------------ + +Git branch : n2p2_training_size +Git revision: 0e4df6f (0e4df6fd828cf869bc711a5277efadde69bdca40) + +Number of OpenMP threads: 4 +******************************************************************************* + +*** SETUP: SETTINGS FILE ****************************************************** + +Settings file name: input.nn +Read 120 lines. +Found 70 lines with keywords. +******************************************************************************* + +*** SETUP: NORMALIZATION ****************************************************** + +Data set normalization is used. +Mean energy per atom : -2.5521343547039809E+01 +Conversion factor energy : 2.4265748255366972E+02 +Conversion factor length : 5.8038448995319847E+00 +******************************************************************************* + +*** SETUP: ELEMENT MAP ******************************************************** + +Number of element strings found: 2 +Element 0: H ( 1) +Element 1: O ( 8) +******************************************************************************* + +*** SETUP: ELEMENTS *********************************************************** + +Number of elements is consistent: 2 +Atomic energy offsets per element: +Element 0: 0.00000000E+00 +Element 1: 0.00000000E+00 +Energy offsets are automatically subtracted from reference energies. +******************************************************************************* + +*** SETUP: CUTOFF FUNCTIONS *************************************************** + +Parameter alpha for inner cutoff: 0.000000 +Inner cutoff = Symmetry function cutoff * alpha +Equal cutoff function type for all symmetry functions: +CutoffFunction::CT_TANHU (2) +f(r) = tanh^3(1 - r/rc) +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTIONS ************************************************* + +Abbreviations: +-------------- +ind .... Symmetry function index. +ec ..... Central atom element. +ty ..... Symmetry function type. +e1 ..... Neighbor 1 element. +e2 ..... Neighbor 2 element. +eta .... Gaussian width eta. +rs ..... Shift distance of Gaussian. +la ..... Angle prefactor lambda. +zeta ... Angle term exponent zeta. +rc ..... Cutoff radius. +ct ..... Cutoff type. +ca ..... Cutoff alpha. +ln ..... Line number in settings file. + +Short range atomic symmetry functions element H : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln +------------------------------------------------------------------------------- + 1 H 2 H 1.000E-03 0.000E+00 1.200E+01 2 0.00 51 + 2 H 2 O 1.000E-03 0.000E+00 1.200E+01 2 0.00 61 + 3 H 2 H 1.000E-02 0.000E+00 1.200E+01 2 0.00 52 + 4 H 2 O 1.000E-02 0.000E+00 1.200E+01 2 0.00 62 + 5 H 2 H 3.000E-02 0.000E+00 1.200E+01 2 0.00 53 + 6 H 2 O 3.000E-02 0.000E+00 1.200E+01 2 0.00 63 + 7 H 2 H 6.000E-02 0.000E+00 1.200E+01 2 0.00 54 + 8 H 2 O 6.000E-02 0.000E+00 1.200E+01 2 0.00 64 + 9 H 2 O 1.500E-01 9.000E-01 1.200E+01 2 0.00 65 + 10 H 2 H 1.500E-01 1.900E+00 1.200E+01 2 0.00 55 + 11 H 2 O 3.000E-01 9.000E-01 1.200E+01 2 0.00 66 + 12 H 2 H 3.000E-01 1.900E+00 1.200E+01 2 0.00 56 + 13 H 2 O 6.000E-01 9.000E-01 1.200E+01 2 0.00 67 + 14 H 2 H 6.000E-01 1.900E+00 1.200E+01 2 0.00 57 + 15 H 2 O 1.500E+00 9.000E-01 1.200E+01 2 0.00 68 + 16 H 2 H 1.500E+00 1.900E+00 1.200E+01 2 0.00 58 + 17 H 3 O O 1.000E-03 0.000E+00 -1 4.0 1.200E+01 2 0.00 115 + 18 H 3 O O 1.000E-03 0.000E+00 1 4.0 1.200E+01 2 0.00 114 + 19 H 3 H O 1.000E-02 0.000E+00 -1 4.0 1.200E+01 2 0.00 105 + 20 H 3 H O 1.000E-02 0.000E+00 1 4.0 1.200E+01 2 0.00 103 + 21 H 3 H O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 100 + 22 H 3 O O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 113 + 23 H 3 H O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 98 + 24 H 3 O O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 112 + 25 H 3 H O 7.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 95 + 26 H 3 H O 7.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 93 + 27 H 3 H O 2.000E-01 0.000E+00 1 1.0 1.200E+01 2 0.00 90 +------------------------------------------------------------------------------- +Short range atomic symmetry functions element O : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln +------------------------------------------------------------------------------- + 1 O 2 H 1.000E-03 0.000E+00 1.200E+01 2 0.00 70 + 2 O 2 O 1.000E-03 0.000E+00 1.200E+01 2 0.00 80 + 3 O 2 H 1.000E-02 0.000E+00 1.200E+01 2 0.00 71 + 4 O 2 O 1.000E-02 0.000E+00 1.200E+01 2 0.00 81 + 5 O 2 H 3.000E-02 0.000E+00 1.200E+01 2 0.00 72 + 6 O 2 O 3.000E-02 0.000E+00 1.200E+01 2 0.00 82 + 7 O 2 H 6.000E-02 0.000E+00 1.200E+01 2 0.00 73 + 8 O 2 O 6.000E-02 0.000E+00 1.200E+01 2 0.00 83 + 9 O 2 H 1.500E-01 9.000E-01 1.200E+01 2 0.00 74 + 10 O 2 O 1.500E-01 4.000E+00 1.200E+01 2 0.00 84 + 11 O 2 H 3.000E-01 9.000E-01 1.200E+01 2 0.00 75 + 12 O 2 O 3.000E-01 4.000E+00 1.200E+01 2 0.00 85 + 13 O 2 H 6.000E-01 9.000E-01 1.200E+01 2 0.00 76 + 14 O 2 O 6.000E-01 4.000E+00 1.200E+01 2 0.00 86 + 15 O 2 H 1.500E+00 9.000E-01 1.200E+01 2 0.00 77 + 16 O 2 O 1.500E+00 4.000E+00 1.200E+01 2 0.00 87 + 17 O 3 H O 1.000E-03 0.000E+00 -1 4.0 1.200E+01 2 0.00 110 + 18 O 3 O O 1.000E-03 0.000E+00 -1 4.0 1.200E+01 2 0.00 120 + 19 O 3 H O 1.000E-03 0.000E+00 1 4.0 1.200E+01 2 0.00 109 + 20 O 3 O O 1.000E-03 0.000E+00 1 4.0 1.200E+01 2 0.00 119 + 21 O 3 H H 1.000E-02 0.000E+00 -1 4.0 1.200E+01 2 0.00 104 + 22 O 3 H H 1.000E-02 0.000E+00 1 4.0 1.200E+01 2 0.00 102 + 23 O 3 H H 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 99 + 24 O 3 H O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 108 + 25 O 3 O O 3.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 118 + 26 O 3 H H 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 97 + 27 O 3 H O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 107 + 28 O 3 O O 3.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 117 + 29 O 3 H H 7.000E-02 0.000E+00 -1 1.0 1.200E+01 2 0.00 94 + 30 O 3 H H 7.000E-02 0.000E+00 1 1.0 1.200E+01 2 0.00 92 +------------------------------------------------------------------------------- +Minimum cutoff radius for element H: 12.000000 +Minimum cutoff radius for element O: 12.000000 +Maximum cutoff radius (global) : 12.000000 +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTION GROUPS ******************************************* + +Abbreviations: +-------------- +ind .... Symmetry function group index. +ec ..... Central atom element. +ty ..... Symmetry function type. +e1 ..... Neighbor 1 element. +e2 ..... Neighbor 2 element. +eta .... Gaussian width eta. +rs ..... Shift distance of Gaussian. +la ..... Angle prefactor lambda. +zeta ... Angle term exponent zeta. +rc ..... Cutoff radius. +ct ..... Cutoff type. +ca ..... Cutoff alpha. +ln ..... Line number in settings file. +mi ..... Member index. +sfi .... Symmetry function index. +e ...... Recalculate exponential term. + +Short range atomic symmetry function groups element H : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln mi sfi e +------------------------------------------------------------------------------- + 1 H 2 H * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 50 1 1 + - - - - 1.000E-02 0.000E+00 - - - 51 2 3 + - - - - 3.000E-02 0.000E+00 - - - 52 3 5 + - - - - 6.000E-02 0.000E+00 - - - 53 4 7 + - - - - 1.500E-01 1.900E+00 - - - 54 5 10 + - - - - 3.000E-01 1.900E+00 - - - 55 6 12 + - - - - 6.000E-01 1.900E+00 - - - 56 7 14 + - - - - 1.500E+00 1.900E+00 - - - 57 8 16 + 2 H 2 O * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 60 1 2 + - - - - 1.000E-02 0.000E+00 - - - 61 2 4 + - - - - 3.000E-02 0.000E+00 - - - 62 3 6 + - - - - 6.000E-02 0.000E+00 - - - 63 4 8 + - - - - 1.500E-01 9.000E-01 - - - 64 5 9 + - - - - 3.000E-01 9.000E-01 - - - 65 6 11 + - - - - 6.000E-01 9.000E-01 - - - 66 7 13 + - - - - 1.500E+00 9.000E-01 - - - 67 8 15 + 3 H 3 H O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-02 0.000E+00 -1 4.0 - - - 104 1 19 1 + - - - - - 1.000E-02 0.000E+00 1 4.0 - - - 102 2 20 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 99 3 21 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 97 4 23 0 + - - - - - 7.000E-02 0.000E+00 -1 1.0 - - - 94 5 25 1 + - - - - - 7.000E-02 0.000E+00 1 1.0 - - - 92 6 26 0 + - - - - - 2.000E-01 0.000E+00 1 1.0 - - - 89 7 27 1 + 4 H 3 O O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-03 0.000E+00 -1 4.0 - - - 114 1 17 1 + - - - - - 1.000E-03 0.000E+00 1 4.0 - - - 113 2 18 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 112 3 22 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 111 4 24 0 +------------------------------------------------------------------------------- +Short range atomic symmetry function groups element O : +------------------------------------------------------------------------------- + ind ec ty e1 e2 eta rs la zeta rc ct ca ln mi sfi e +------------------------------------------------------------------------------- + 1 O 2 H * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 69 1 1 + - - - - 1.000E-02 0.000E+00 - - - 70 2 3 + - - - - 3.000E-02 0.000E+00 - - - 71 3 5 + - - - - 6.000E-02 0.000E+00 - - - 72 4 7 + - - - - 1.500E-01 9.000E-01 - - - 73 5 9 + - - - - 3.000E-01 9.000E-01 - - - 74 6 11 + - - - - 6.000E-01 9.000E-01 - - - 75 7 13 + - - - - 1.500E+00 9.000E-01 - - - 76 8 15 + 2 O 2 O * * 1.200E+01 2 0.00 * * * + - - - - 1.000E-03 0.000E+00 - - - 79 1 2 + - - - - 1.000E-02 0.000E+00 - - - 80 2 4 + - - - - 3.000E-02 0.000E+00 - - - 81 3 6 + - - - - 6.000E-02 0.000E+00 - - - 82 4 8 + - - - - 1.500E-01 4.000E+00 - - - 83 5 10 + - - - - 3.000E-01 4.000E+00 - - - 84 6 12 + - - - - 6.000E-01 4.000E+00 - - - 85 7 14 + - - - - 1.500E+00 4.000E+00 - - - 86 8 16 + 3 O 3 H H * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-02 0.000E+00 -1 4.0 - - - 103 1 21 1 + - - - - - 1.000E-02 0.000E+00 1 4.0 - - - 101 2 22 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 98 3 23 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 96 4 26 0 + - - - - - 7.000E-02 0.000E+00 -1 1.0 - - - 93 5 29 1 + - - - - - 7.000E-02 0.000E+00 1 1.0 - - - 91 6 30 0 + 4 O 3 H O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-03 0.000E+00 -1 4.0 - - - 109 1 17 1 + - - - - - 1.000E-03 0.000E+00 1 4.0 - - - 108 2 19 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 107 3 24 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 106 4 27 0 + 5 O 3 O O * * * * 1.200E+01 2 0.00 * * * * + - - - - - 1.000E-03 0.000E+00 -1 4.0 - - - 119 1 18 1 + - - - - - 1.000E-03 0.000E+00 1 4.0 - - - 118 2 20 0 + - - - - - 3.000E-02 0.000E+00 -1 1.0 - - - 117 3 25 1 + - - - - - 3.000E-02 0.000E+00 1 1.0 - - - 116 4 28 0 +------------------------------------------------------------------------------- +******************************************************************************* + +*** SETUP: NEURAL NETWORKS **************************************************** + +Normalize neurons (all elements): 0 +------------------------------------------------------------------------------- +Atomic short range NN for element H : +Number of weights : 1325 +Number of biases : 51 +Number of connections: 1376 +Architecture 27 25 25 1 +------------------------------------------------------------------------------- + 1 G t t l + 2 G t t + 3 G t t + 4 G t t + 5 G t t + 6 G t t + 7 G t t + 8 G t t + 9 G t t + 10 G t t + 11 G t t + 12 G t t + 13 G t t + 14 G t t + 15 G t t + 16 G t t + 17 G t t + 18 G t t + 19 G t t + 20 G t t + 21 G t t + 22 G t t + 23 G t t + 24 G t t + 25 G t t + 26 G + 27 G +------------------------------------------------------------------------------- +Atomic short range NN for element O : +Number of weights : 1400 +Number of biases : 51 +Number of connections: 1451 +Architecture 30 25 25 1 +------------------------------------------------------------------------------- + 1 G t t l + 2 G t t + 3 G t t + 4 G t t + 5 G t t + 6 G t t + 7 G t t + 8 G t t + 9 G t t + 10 G t t + 11 G t t + 12 G t t + 13 G t t + 14 G t t + 15 G t t + 16 G t t + 17 G t t + 18 G t t + 19 G t t + 20 G t t + 21 G t t + 22 G t t + 23 G t t + 24 G t t + 25 G t t + 26 G + 27 G + 28 G + 29 G + 30 G +------------------------------------------------------------------------------- +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTION SCALING ****************************************** + +Equal scaling type for all symmetry functions: +Scaling type::ST_SCALECENTER (3) +Gs = Smin + (Smax - Smin) * (G - Gmean) / (Gmax - Gmin) +Smin = 0.000000 +Smax = 1.000000 +Symmetry function scaling statistics from file: scaling.data +------------------------------------------------------------------------------- + +Abbreviations: +-------------- +ind ..... Symmetry function index. +min ..... Minimum symmetry function value. +max ..... Maximum symmetry function value. +mean .... Mean symmetry function value. +sigma ... Standard deviation of symmetry function values. +sf ...... Scaling factor for derivatives. +Smin .... Desired minimum scaled symmetry function value. +Smax .... Desired maximum scaled symmetry function value. +t ....... Scaling type. + +Scaling data for symmetry functions element H : +------------------------------------------------------------------------------- + ind min max mean sigma sf Smin Smax t +------------------------------------------------------------------------------- + 1 1.09E+00 9.62E+00 2.27E+00 6.79E-01 1.17E-01 0.00 1.00 3 + 2 7.33E-01 5.00E+00 1.33E+00 3.39E-01 2.34E-01 0.00 1.00 3 + 3 7.60E-01 7.14E+00 1.65E+00 5.08E-01 1.57E-01 0.00 1.00 3 + 4 5.48E-01 3.77E+00 1.02E+00 2.54E-01 3.11E-01 0.00 1.00 3 + 5 4.01E-01 4.15E+00 9.09E-01 2.98E-01 2.67E-01 0.00 1.00 3 + 6 3.62E-01 2.27E+00 6.49E-01 1.48E-01 5.25E-01 0.00 1.00 3 + 7 1.89E-01 2.23E+00 4.57E-01 1.60E-01 4.90E-01 0.00 1.00 3 + 8 2.67E-01 1.32E+00 4.24E-01 8.05E-02 9.49E-01 0.00 1.00 3 + 9 2.45E-01 9.48E-01 3.62E-01 5.30E-02 1.42E+00 0.00 1.00 3 + 10 2.22E-01 2.76E+00 5.39E-01 2.01E-01 3.94E-01 0.00 1.00 3 + 11 1.47E-01 5.56E-01 2.68E-01 2.62E-02 2.45E+00 0.00 1.00 3 + 12 9.91E-02 1.73E+00 2.96E-01 1.16E-01 6.14E-01 0.00 1.00 3 + 13 6.51E-02 3.45E-01 1.85E-01 1.97E-02 3.57E+00 0.00 1.00 3 + 14 3.17E-02 9.13E-01 1.50E-01 5.35E-02 1.13E+00 0.00 1.00 3 + 15 2.92E-03 2.65E-01 7.65E-02 1.88E-02 3.82E+00 0.00 1.00 3 + 16 3.21E-04 2.87E-01 4.58E-02 2.33E-02 3.49E+00 0.00 1.00 3 + 17 2.47E-04 1.38E-01 1.77E-02 9.75E-03 7.23E+00 0.00 1.00 3 + 18 5.10E-03 5.83E-01 2.39E-02 3.78E-02 1.73E+00 0.00 1.00 3 + 19 3.23E-04 2.16E-01 1.71E-02 1.40E-02 4.63E+00 0.00 1.00 3 + 20 4.96E-02 1.69E+00 1.45E-01 1.10E-01 6.11E-01 0.00 1.00 3 + 21 3.41E-03 3.16E-01 1.84E-02 2.01E-02 3.20E+00 0.00 1.00 3 + 22 1.31E-04 1.03E-01 6.37E-03 6.61E-03 9.76E+00 0.00 1.00 3 + 23 3.38E-02 9.16E-01 8.13E-02 5.79E-02 1.13E+00 0.00 1.00 3 + 24 4.17E-04 1.58E-01 4.66E-03 9.86E-03 6.35E+00 0.00 1.00 3 + 25 7.35E-04 5.92E-02 3.70E-03 3.31E-03 1.71E+01 0.00 1.00 3 + 26 8.98E-03 1.94E-01 2.41E-02 1.10E-02 5.40E+00 0.00 1.00 3 + 27 2.12E-04 8.78E-03 2.06E-03 5.88E-04 1.17E+02 0.00 1.00 3 +------------------------------------------------------------------------------- +Scaling data for symmetry functions element O : +------------------------------------------------------------------------------- + ind min max mean sigma sf Smin Smax t +------------------------------------------------------------------------------- + 1 1.51E+00 1.00E+01 2.65E+00 6.78E-01 1.18E-01 0.00 1.00 3 + 2 4.44E-01 4.62E+00 9.66E-01 3.37E-01 2.39E-01 0.00 1.00 3 + 3 1.19E+00 7.53E+00 2.03E+00 5.06E-01 1.58E-01 0.00 1.00 3 + 4 2.76E-01 3.39E+00 6.59E-01 2.50E-01 3.21E-01 0.00 1.00 3 + 5 8.06E-01 4.54E+00 1.30E+00 2.94E-01 2.68E-01 0.00 1.00 3 + 6 1.05E-01 1.89E+00 3.07E-01 1.42E-01 5.60E-01 0.00 1.00 3 + 7 5.69E-01 2.62E+00 8.48E-01 1.57E-01 4.89E-01 0.00 1.00 3 + 8 2.33E-02 9.36E-01 1.11E-01 6.98E-02 1.10E+00 0.00 1.00 3 + 9 5.14E-01 1.85E+00 7.25E-01 9.80E-02 7.46E-01 0.00 1.00 3 + 10 1.11E-01 2.91E+00 4.75E-01 2.34E-01 3.57E-01 0.00 1.00 3 + 11 3.53E-01 1.07E+00 5.35E-01 4.52E-02 1.39E+00 0.00 1.00 3 + 12 3.04E-02 2.53E+00 3.17E-01 2.10E-01 4.00E-01 0.00 1.00 3 + 13 1.60E-01 6.63E-01 3.70E-01 3.08E-02 1.99E+00 0.00 1.00 3 + 14 2.78E-03 2.30E+00 1.77E-01 1.86E-01 4.35E-01 0.00 1.00 3 + 15 9.56E-03 3.91E-01 1.53E-01 2.79E-02 2.62E+00 0.00 1.00 3 + 16 3.75E-06 2.04E+00 5.41E-02 1.43E-01 4.91E-01 0.00 1.00 3 + 17 2.47E-03 3.43E-01 1.67E-02 2.19E-02 2.93E+00 0.00 1.00 3 + 18 1.74E-05 5.63E-02 9.55E-04 3.36E-03 1.78E+01 0.00 1.00 3 + 19 5.48E-02 3.02E+00 2.04E-01 2.01E-01 3.37E-01 0.00 1.00 3 + 20 1.38E-03 4.99E-01 1.28E-02 3.18E-02 2.01E+00 0.00 1.00 3 + 21 6.69E-03 2.67E-01 3.09E-02 1.71E-02 3.84E+00 0.00 1.00 3 + 22 1.70E-02 1.42E+00 7.63E-02 9.29E-02 7.14E-01 0.00 1.00 3 + 23 1.98E-02 4.08E-01 4.88E-02 2.55E-02 2.58E+00 0.00 1.00 3 + 24 5.28E-04 2.33E-01 7.21E-03 1.45E-02 4.30E+00 0.00 1.00 3 + 25 1.11E-05 3.53E-02 4.25E-04 2.05E-03 2.83E+01 0.00 1.00 3 + 26 1.60E-02 8.22E-01 5.08E-02 5.28E-02 1.24E+00 0.00 1.00 3 + 27 3.99E-03 7.86E-01 3.69E-02 5.05E-02 1.28E+00 0.00 1.00 3 + 28 4.05E-05 9.84E-02 1.21E-03 5.79E-03 1.02E+01 0.00 1.00 3 + 29 6.04E-03 9.93E-02 1.62E-02 5.52E-03 1.07E+01 0.00 1.00 3 + 30 2.96E-03 1.55E-01 1.16E-02 8.94E-03 6.59E+00 0.00 1.00 3 +------------------------------------------------------------------------------- +******************************************************************************* + +*** SETUP: NEURAL NETWORK WEIGHTS ********************************************* + +Weight file name format: weights.%03zu.data +Weight file for element H: weights.001.data +Weight file for element O: weights.008.data +******************************************************************************* + +*** SETUP: SYMMETRY FUNCTION STATISTICS *************************************** + +Equal symmetry function statistics for all elements. +Collect min/max/mean/sigma : 0 +Collect extrapolation warnings : 0 +Write extrapolation warnings immediately to stderr: 1 +Halt on any extrapolation warning : 0 +******************************************************************************* + +*** PREDICTION **************************************************************** + +Reading structure file... +Structure contains 1080 atoms (2 elements). +Calculating NNP prediction... + +------------------------------------------------------------------------------- +NNP energy: -2.75645473E+04 + +NNP forces: + 1 O -2.89660301E-02 2.72095998E-03 3.97370214E-03 + 2 H 7.98774238E-03 3.32976047E-03 -1.01638330E-02 + 3 H 1.18069028E-02 -9.49625097E-03 6.63343658E-03 + 4 O 1.58114603E-03 1.50108414E-03 -1.91253456E-02 + 5 H -5.59046661E-03 8.94407619E-03 -2.23070103E-03 + 6 H 3.01737375E-04 -1.01259575E-02 1.38573559E-02 + 7 O -7.80967126E-04 -1.67088140E-03 -4.69678066E-03 + 8 H 2.24274610E-03 -5.08242407E-03 2.83147010E-03 + 9 H 1.43702093E-03 6.00233905E-03 6.12526798E-04 + 10 O -2.34704279E-03 -3.63775225E-02 -2.51160718E-02 + 11 H -5.40416012E-03 2.60317761E-02 1.36993832E-02 + 12 H 2.23180592E-02 1.15764132E-02 1.18816498E-02 + 13 O -3.72110476E-03 -3.05350380E-02 -6.94183042E-03 + 14 H -1.39535531E-03 3.00144161E-02 2.74800415E-03 + 15 H -1.04113988E-03 2.78821511E-04 4.41910314E-03 + 16 O -3.36659879E-03 -6.85772887E-03 -2.88767842E-02 + 17 H -3.99916640E-03 2.40985859E-03 1.99008676E-02 + 18 H 9.59840171E-03 3.97233732E-03 1.67574856E-02 + 19 O 3.22197501E-02 -2.52329644E-02 1.18698760E-02 + 20 H -3.21395032E-02 1.51632487E-02 -5.81784495E-03 + 21 H 2.48547399E-03 1.21719766E-02 -1.35947616E-02 + 22 O -2.43669841E-02 4.05657359E-03 -1.86306088E-02 + 23 H 3.21454294E-03 -4.60213908E-03 1.64444334E-02 + 24 H 1.76727492E-02 -5.47746421E-03 6.21346797E-03 + 25 O -8.56700703E-03 -1.63220150E-02 1.53058105E-02 + 26 H 1.56239186E-02 1.29074520E-02 -8.92099219E-03 + 27 H 2.52448398E-03 2.44424160E-03 -1.07191828E-02 + 28 O -2.28918176E-02 2.57872401E-02 3.85240863E-02 + 29 H 1.92178374E-02 3.22692072E-03 -2.27934792E-02 + 30 H -1.10305838E-03 -3.17109524E-02 -1.14006786E-02 + 31 O 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-2.7564547347815904E+04 +charge 0.0000000000000000E+00 +end diff --git a/src/doc/sphinx/Makefile b/src/doc/sphinx/Makefile index b344d9576..68be1725c 100644 --- a/src/doc/sphinx/Makefile +++ b/src/doc/sphinx/Makefile @@ -3,7 +3,7 @@ # You can set these variables from the command line. SPHINXOPTS = -SPHINXBUILD = sphinx-build +SPHINXBUILD = python3 -msphinx SPHINXPROJ = n2p2-Aneuralnetworkpotentialpackage SOURCEDIR = source BUILDDIR = ../../../doc/sphinx/ diff --git a/src/doc/sphinx/source/Tools/NNTSSD.rst b/src/doc/sphinx/source/Tools/NNTSSD.rst new file mode 100755 index 000000000..53312f246 --- /dev/null +++ b/src/doc/sphinx/source/Tools/NNTSSD.rst @@ -0,0 +1,278 @@ +.. _NNTSSD: + +NNTSSD +========== + +The purpose of the ``NNTSSD`` tool is to analyse the training set size dependence of residual error of neural network potenials. It is specifically written to be used with ``n2p2``. + + +Structure: +---------- + +``NNTSSD`` provides a three classes consisting of two to four main methods: + +#. ``NNTSSD.Tools`` class: + * ``create_training_datasets`` creates training datasets of different size from a given original dataset using the tool :ref:`nnp-select`. + * ``training_neural_network`` trains the neural network with different existing datasets using :ref:`training`. + * ``analyse_learning_curves`` prepares anaylse data from the learning curves obtained in training. + * ``plot_size_dependence`` plots the energy and forces RMSE versus training set size. +#. ``NNTSSD.Validation`` class: + * ``predict_validation_data`` predicts the energies and forces of a validation dataset for all trained neural networks. + * ``plot_validation_data`` plots the energy and force RMSE versus training set size. +#. ``NNTSSD.External_Testdata`` class: + * ``predict_test_data`` predicts the energies and forces of a test dataset for all trained neural networks. + * ``analyse_learning_curves`` prepares analyse data from the learning curves obtained in training neural network and predicting testdata. + * ``plot_test_size_dependence`` plots the energy and force RMSE versus training set size. + +* ``perform_NNTSSD`` performs NNTSSD methods according to user-given specifications. + It uses the module ``file_input`` to read user-given parameters. + +For more detailed information about the classes and methods, please see the code documentation for ``NNTSSD``. + +There are some additional functionalities provided in the following programs that might be useful. + +* ``split_data.py`` splits the file input.data in two files with respect to given fraction. It may be used to create an external testset, validation dataset, or both. +* ``file_input.py`` contains tools for reading the NNTSSD parameters from file ``NNTSSD_input.dat`` (see ``Usage`` for further information). +* ``learning_curves.py`` provides tools for analysing learning curves and plotting training performance. By default, learning curves obtained from external testdata are used. If they are not available, learning curves obtained from internal testdata are used. + + +Build instructions: +------------------- + +To use the ``NNTSSD`` tool, please first build ``n2p2`` according to the build instructions on the home page of this documentation. + +The code for the ``NNTSSD`` tool is written in python3_ and uses the packages os_, sys_, shutil_, numpy_ and matplotlib_. + +The code documentation for NNTSSD can be built by executing + + .. code-block:: none + + make html + +in the directory ~/n2p2/tools/python/NNTSSD/docs/. + +Depending on which methods shall be executed, there are some files and folders expected to be located in the current working directory. Please see the code documentation for further information. + + +Guidance: +--------- + +A rough guidance of how ``NNTSSD`` is designed to be used. We assume there exists one large original dataset that shall be used for analysing the training set size dependence of residual error of ``n2p2``: + +#. (If validation dataset and/or external testdata shall be used) Use ``split_data.py`` to split the original dataset in two/three datasets: the ``training dataset`` (which automatically includes the internal testdata), the ``validation dataset`` and/or the ``external testdata``. +#. Copy the ``training dataset`` renamed as ``input.data`` into a directory of desired name that must be located in ``~/n2p2/tools/python/NNTSSD/`` (in the following, this directory will be refered to as ``working directory``). In the ``working directory``, run the method ``Tools.create_training_datasets``. (A user friendly way how to "run" ``NNTSSD`` methods is described in the next section ``Usage``. Basically, just fill in the file ``NNTSSD_input.dat`` as desired and run ``NNTSSD.py`` in the ``working directory``) +#. Copy the ``n2p2`` files ``input.nn`` and ``scaling.data`` into the ``working directory`` and run the method ``Tools.training_neural_network``. +#. (If desired) Run the methods ``Tools.analyse_learning_curves`` and ``Tools.plot_size_dependence``. The analysis is done using internal testsets only (no external testset, no validation dataset). +#. (If an external testset shall be used) Create a directory ``predict_test_data`` inside the ``working directory``. Into it, copy the ``external testdata`` renamed as ``input.data`` and the files ``input.nn``and ``scaling.data``. In the ``working directory``, run the methods ``External_Testdata.predict_test_data``, ``External_Testdata.analyse_learning_curves`` and ``External_Testdata.plot_test_size_dependence``. +#. (If a validation dataset shall be used) Create a directory ``validation_data`` inside the ``working directory``. Into it, copy the ``validation dataset`` renamed as ``input.data``, as well as the files ``input.nn`` and ``scaling.data``. In the ``working directory``, run the methods ``Validation.predict_validation_data``, ``Validation.plot_validation_data``. + + +Usage: +------ + +* + The four main methods listed above can of course easily be used by calling them, eg. in a python3_ console, or can be runned automatically in a short script. + + +* + However, for some applications it might be convenient to use the function ``perform_NNTSSD`` which performs NNTSSD methods according to user-given specifications. Firstly, it reads the NNTSSD parameters from the file ``NNTSSD_input.dat`` using the module ``file_input.dat``. Secondly, it performs a user-given selection of the main NNTSSD methods listed above. + For reading the input file, the method ``file_input.py`` is used. It requires a file ``NNTSSD_input.dat`` of the following self-explaining form: + + .. code-block:: none + + # INPUT SPECIFICATIONS FOR RUNNING NNTSSD: + ################################################################################ + # Specify which NNTSSD.Tools steps shall be performed: + ################################################################################ + # Create datasets/training/analyse learning curves/plot size dependence? + # (y/n, separated by blank space): + y y y y + # In case of external testdata: predict data/analyse learning curves/plot SSD? + y y y + # Predict validation data/plot size dependence? + y y + ################################################################################ + # Specify parameters for NNTSSD.Tools.create_training_datatsets: + ################################################################################ + # Give a list of the desired set size ratios, separated by blank space: + + # Alternatively: Give minimum, maximum and step size of desired ratios, + # separated by blank space (used if above is empty): + 0.8 0.9 0.1 + # Give the number of sample datasets per training size: + 2 + # Do you wish to fix the random generator seed to a specific value? (y/n) + n + # If yes, give the random generator seed (optional, default is 123): + 123 + ################################################################################ + # Specify parameters for NNTSSD.Tools.training_neural_network: + ################################################################################ + # Give the desired number of training epochs: + 4 + # Give the fraction of data you want to keep for testing (nnp-internal): + 0.1 + # Give the number of cores you want to use for mpirun (nnp-train): + 4 + # Write a VSC submission script (if not, training is performed on your machine)? + n + # If yes, give the maximum time required for exectuing job (hh:mm:ss, optional): + 00:10:00 + # Do you wish to fix the random generator seed to a specific value? (y/n) + n + # If yes, give the random generator seed (optional, default is 123): + 789 + ################################################################################ + # Specify parameters for NNTSSD.External_Testdata.predict_test_data: + ################################################################################ + # Give the number of cores you want to use for mpirun (nnp-dataset): + 4 + # Do you wish to fix the random generator seed to a specific value? (y/n) + n + # If yes, give the random generator seed (optional, default is 123): + 789 + ################################################################################ + # Specify parameters for NNTSSD.Validation.predict_validation_data: + ################################################################################ + # Give the number of cores you want to use for mpirun (nnp-dataset): + 4 + # Do you wish to fix the random generator seed to a specific value? (y/n) + n + # If yes, give the random generator seed (optional, default is 123): + 789 + +Example: +--------- + +There is a short example prepared. In the directory ``NNTSSD/example/``, run the following command + + .. code-block:: none + + python3 ../source/NNTSSD.py + + +Sample screen output: +--------------------- + +.. code-block:: none + + ********************************************************************** + NNTSSD - Tools for Neural Network Training Set Size Dependence + ********************************************************************** + Performing the following NNTSSD steps: + True Tools Create training datasets + True Tools Training neural network + True Tools Analyse learning curves + True Tools Plot size dependence + True External Testset Predicting external testset + True External Testset Analyse learning curves wrt external testset + True External Testset Plot size dependence wrt external testset + True Validation Predicting and analysing validation dataset + True Validation Plot size dependence wrt validation dataset + + ***CREATING TRAINING DATASETS*************************************************** + number of samples per training set size = 2 + number of different training set sizes = 2 + We are working with ratio 0.80 + ../../../../bin/nnp-select random 0.80 128 >/dev/null + ../../../../bin/nnp-select random 0.80 978 >/dev/null + We are working with ratio 0.90 + ../../../../bin/nnp-select random 0.90 918 >/dev/null + ../../../../bin/nnp-select random 0.90 215 >/dev/null + INFO: Removed old 'Output' folder. + FINISHED creating datasets. + + ***TRAINING NEURAL NETWORK***************************************************** + We are working with ratio 0.80 + mpirun -np 4 ../../../../../../../bin/nnp-train >/dev/null + mpirun -np 4 ../../../../../../../bin/nnp-train >/dev/null + We are working with ratio 0.90 + mpirun -np 4 ../../../../../../../bin/nnp-train >/dev/null + mpirun -np 4 ../../../../../../../bin/nnp-train >/dev/null + FINISHED training with 2 different ratios. + + ***ANALYSING LEARNING CURVES*************************************************** + Analysing data at epoch of minimum energy + We are working with ratio 0.80 + We are working with ratio 0.90 + Analysing data at epoch of minimum force + We are working with ratio 0.80 + We are working with ratio 0.90 + Analysing data at epoch of minimum comb + We are working with ratio 0.80 + We are working with ratio 0.90 + FINISHED analysing learning curves. + + ***PLOTTING SIZE DEPENDENCE**************************************************** + Plotting size dependence. + FINISHED plotting size dependence. + + ***PREDICTING EXTERNAL TESTDATA************************************************ + We are working with ratio 0.80 + We are working with ratio 0.90 + FINISHED predicting test data. + + ***ANALYSING LEARNING CURVES W/ EXTERNAL TESTDATA****************************** + Analysing data at epoch of minimum energy + We are working with ratio 0.80 + We are working with ratio 0.90 + Analysing data at epoch of minimum force + We are working with ratio 0.80 + We are working with ratio 0.90 + Analysing data at epoch of minimum comb + We are working with ratio 0.80 + We are working with ratio 0.90 + FINISHED analysing learning curves. + + ***PLOTTING TEST SIZE DEPENDENCE W/ EXTERNAL TESTDATA************************** + Plotting test size dependence. + FINISHED plotting test size dependence. + + ***PREDICTING VALIDATION DATA************************************************ + Analysing data at epoch of minimum energy + We are working with ratio 0.80 + We are working with ratio 0.90 + Analysing data at epoch of minimum force + We are working with ratio 0.80 + We are working with ratio 0.90 + Analysing data at epoch of minimum comb + We are working with ratio 0.80 + We are working with ratio 0.90 + FINISHED predicting validation data. + + ***PLOTTING TEST SIZE DEPENDENCE W/ VALIDATION DATA**************************** + Plotting test size dependence. + FINISHED plotting test size dependence. + + +To see the ``n2p2`` screen outputs from :ref:`nnp-select`, :ref:`training` and ``nnp-dataset``, simply remove the addition ``>/dev/null`` in the ``NNTSSD`` code after ``n2p2`` commands. (The user documentation for ``nnp-dataset`` is not yet available, but will be linked here. It works very similar to :ref:`nnp-predict`, but for datasets instead of single configurations). + + +File output: +------------ + +``NNTSSD`` creates a directory ``Output`` containing a 2-layered directory structure (3-layered in case of external testdata or a validation dataset) and various files. See the code documentation for explanation of the specific files each method creates. +The final results are plotted and saved as *.png files. If no external testdata or validation dataset is used, the *.png files are + +* ``Output/int_Energy_RMSE_epoch_comparison.png``\ : Shows train and test energy RMSE (and its standard deviation) versus training set size, for the case of internal testdata and comparing three epoch optimization approaches. +* ``Output/int_Forces_RMSE_epoch_comparison.png`` : Shows train and test forces RMSE (and its standard deviation) versus training set size, for the case of internal testdata and comparing three epoch optimization approaches. + +For a more detailed description of which *.png files are saved if external testdata or validation datasets are used, please see the code documentation of ``NNTSSD``. + + +Tests: +------ + +Tests are prepared and can be used to check whether ``NNTSSD`` works and creates the correct outputs. +In the directory ``NNTSSD/tests/``, tun the following command + + .. code-block:: none + + python3 -m pytest + +.. _python3: https://www.python.org/ +.. _os: https://docs.python.org/3/library/os.html +.. _sys: https://docs.python.org/3/library/sys.html +.. _shutil: https://docs.python.org/3/library/shutil.html +.. _numpy: https://www.numpy.org/ +.. _matplotlib: https://matplotlib.org/ diff --git a/src/doc/sphinx/source/index.rst b/src/doc/sphinx/source/index.rst index 8b585b589..2415f5d40 100644 --- a/src/doc/sphinx/source/index.rst +++ b/src/doc/sphinx/source/index.rst @@ -310,6 +310,7 @@ list of keywords is provided :ref:`here `. Tools/nnp-prune Tools/nnp-select Tools/nnp-scaling + Tools/NNTSSD .. toctree:: :hidden: diff --git a/src/libnnp/version.h b/src/libnnp/version.h index 78eab67bc..29fa629dc 100644 --- a/src/libnnp/version.h +++ b/src/libnnp/version.h @@ -18,8 +18,8 @@ #define VERSION_H #define NNP_VERSION "2.0.0" -#define NNP_GIT_REV "fbccbe5689e038af280340e3871df930b1f02fb0" -#define NNP_GIT_REV_SHORT "fbccbe5" -#define NNP_GIT_BRANCH "singraber-master" +#define NNP_GIT_REV "d35e871fc0709a30a9edcf674e3950eb63294953" +#define NNP_GIT_REV_SHORT "d35e871" +#define NNP_GIT_BRANCH "n2p2_training_size" #endif diff --git a/src/makefile.gnu b/src/makefile.gnu index 5786ee1f7..c5a776e01 100644 --- a/src/makefile.gnu +++ b/src/makefile.gnu @@ -6,7 +6,7 @@ # Enter here paths to GSL or EIGEN if they are not in your standard include # path. DO NOT completely remove the entry, leave at least "./". PROJECT_GSL=./ -PROJECT_EIGEN=${HOME}/local/src/eigen-eigen-5a0156e40feb +PROJECT_EIGEN=${HOME}/Documents/eigen-eigen-323c052e1731/ ############################################################################### # COMPILERS AND FLAGS diff --git a/tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb b/tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb new file mode 100644 index 000000000..39eae4d8a --- /dev/null +++ b/tools/python/NNTSSD/.ipynb_checkpoints/0_Pre_FirstTry-checkpoint.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "08.04.2019, First Attempt.\n", + "\"\"\"\n", + "\n", + "import pynnp\n", + "import os\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of samples per training set size = 2\n", + "number of different training set sizes = 3\n", + "0.05\n", + "0.1\n", + "0.15\n" + ] + } + ], + "source": [ + "def create_training_datasets(set_size_ratios,n_sets_per_size):\n", + " n_set_size_ratios = np.size(set_size_ratios)\n", + " print \"number of samples per training set size = \", n_sets_per_size\n", + " print \"number of different training set sizes = \", n_set_size_ratios\n", + " old_name = \"current_folder\"\n", + " for ratios_counter in range(n_set_size_ratios):\n", + " current_ratio = set_size_ratios[ratios_counter]\n", + " for sets_per_size_counter in range(n_sets_per_size):\n", + " os.system(\"mkdir current_folder\")\n", + " new_name = \"ratio\"+str(current_ratio)+\"_set\"+str(sets_per_size_counter)\n", + " os.rename(old_name,new_name)\n", + " print current_ratio\n", + "\n", + "create_training_datasets([0.05,0.10,0.15],2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 000000000..2fd64429b --- /dev/null +++ b/tools/python/NNTSSD/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/docs/Makefile b/tools/python/NNTSSD/docs/Makefile new file mode 100644 index 000000000..69fe55ecf --- /dev/null +++ b/tools/python/NNTSSD/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/tools/python/NNTSSD/docs/make.bat b/tools/python/NNTSSD/docs/make.bat new file mode 100644 index 000000000..543c6b13b --- /dev/null +++ b/tools/python/NNTSSD/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/tools/python/NNTSSD/docs/source/NNTSSD.rst b/tools/python/NNTSSD/docs/source/NNTSSD.rst new file mode 100644 index 000000000..5c3990e25 --- /dev/null +++ b/tools/python/NNTSSD/docs/source/NNTSSD.rst @@ -0,0 +1,7 @@ +NNTSSD module +============= + +.. automodule:: NNTSSD + :members: + :undoc-members: + :show-inheritance: diff --git a/tools/python/NNTSSD/docs/source/conf.py b/tools/python/NNTSSD/docs/source/conf.py new file mode 100644 index 000000000..376521e04 --- /dev/null +++ b/tools/python/NNTSSD/docs/source/conf.py @@ -0,0 +1,177 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('../../source')) + + +# -- Project information ----------------------------------------------------- + +project = u'NNTSSD' +copyright = u'2019, Madlen Reiner' +author = u'Madlen Reiner' + +# The short X.Y version +version = u'' +# The full version, including alpha/beta/rc tags +release = u'' + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc','sphinx.ext.napoleon','sphinx.ext.autosummary' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = None + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'NNTSSDdoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'NNTSSD.tex', u'NNTSSD Documentation', + u'Madlen Reiner', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'nntssd', u'NNTSSD Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'NNTSSD', u'NNTSSD Documentation', + author, 'NNTSSD', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + + +# -- Extension configuration ------------------------------------------------- diff --git a/tools/python/NNTSSD/docs/source/file_input.rst b/tools/python/NNTSSD/docs/source/file_input.rst new file mode 100644 index 000000000..60dfd2539 --- /dev/null +++ b/tools/python/NNTSSD/docs/source/file_input.rst @@ -0,0 +1,7 @@ +file\_input module +================== + +.. automodule:: file_input + :members: + :undoc-members: + :show-inheritance: diff --git a/tools/python/NNTSSD/docs/source/index.rst b/tools/python/NNTSSD/docs/source/index.rst new file mode 100644 index 000000000..33be4fe57 --- /dev/null +++ b/tools/python/NNTSSD/docs/source/index.rst @@ -0,0 +1,22 @@ +.. NNTSSD documentation master file, created by + sphinx-quickstart on Wed May 15 14:17:23 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to NNTSSD's code documentation! +================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + +.. toctree:: modules + :caption: Modules: + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/tools/python/NNTSSD/docs/source/interactive_input.rst b/tools/python/NNTSSD/docs/source/interactive_input.rst new file mode 100644 index 000000000..8316860e7 --- /dev/null +++ b/tools/python/NNTSSD/docs/source/interactive_input.rst @@ -0,0 +1,7 @@ +interactive\_input module +========================= + +.. automodule:: interactive_input + :members: + :undoc-members: + :show-inheritance: diff --git a/tools/python/NNTSSD/docs/source/learning_curves.rst b/tools/python/NNTSSD/docs/source/learning_curves.rst new file mode 100644 index 000000000..e485bec0e --- /dev/null +++ b/tools/python/NNTSSD/docs/source/learning_curves.rst @@ -0,0 +1,7 @@ +learning\_curves module +======================= + +.. automodule:: learning_curves + :members: + :undoc-members: + :show-inheritance: diff --git a/tools/python/NNTSSD/docs/source/modules.rst b/tools/python/NNTSSD/docs/source/modules.rst new file mode 100644 index 000000000..6b1bd2933 --- /dev/null +++ b/tools/python/NNTSSD/docs/source/modules.rst @@ -0,0 +1,9 @@ +source +====== + +.. toctree:: + :maxdepth: 4 + + NNTSSD + file_input + learning_curves diff --git a/tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb b/tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb new file mode 100644 index 000000000..19b875acf --- /dev/null +++ b/tools/python/NNTSSD/example/.ipynb_checkpoints/NNTSSD_example-checkpoint.ipynb @@ -0,0 +1,66 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example for running NNTSSD\n", + "(Tools for analysing the Neural Network Training Set Size Dependence)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NNTSSD performs one or more of four steps:\n", + "
1.) It creates training datasets from a given original dataset using the program nnp-select.\n", + "
2.) It trains the neural network with existing datasets using the program nnp-train.\n", + "
3.) It prepares analyse data from the learning curves.\n", + "
4.) It plots the energy and force RMSE versus training set size.\n", + "

In the following, we will go through each of these steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.) Create Training Datatsets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 000000000..2fd64429b --- /dev/null +++ b/tools/python/NNTSSD/example/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/python/NNTSSD/example/NNTSSD_input.dat b/tools/python/NNTSSD/example/NNTSSD_input.dat new file mode 100644 index 000000000..6c2b90e28 --- /dev/null +++ b/tools/python/NNTSSD/example/NNTSSD_input.dat @@ -0,0 +1,60 @@ +# INPUT SPECIFICATIONS FOR RUNNING NNTSSD: +################################################################################ +# Specify which NNTSSD.Tools steps shall be performed: +################################################################################ +# Create datasets/training/analyse learning curves/plot size dependence? +# (y/n, separated by blank space): +y y y y +# In case of external testdata: predict data/analyse learning curves/plot SSD? +y y y +# Predict validation data/plot size dependence? +y y +################################################################################ +# Specify parameters for NNTSSD.Tools.create_training_datatsets: +################################################################################ +# Give a list of the desired set size ratios, separated by blank space: + +# Alternatively: Give minimum, maximum and step size of desired ratios, +# separated by blank space (used if above is empty): +0.8 0.9 0.1 +# Give the number of sample datasets per training size: +2 +# Do you wish to fix the random generator seed to a specific value? (y/n) +n +# If yes, give the random generator seed (optional, default is 123): +123 +################################################################################ +# Specify parameters for NNTSSD.Tools.training_neural_network: +################################################################################ +# Give the desired number of training epochs: +4 +# Give the fraction of data you want to keep for testing (nnp-internal): +0.1 +# Give the number of cores you want to use for mpirun (nnp-train): +4 +# Write a VSC submission script (if not, training is performed on your machine)? +n +# If yes, give the maximum time required for exectuing job (hh:mm:ss, optional): +00:10:00 +# Do you wish to fix the random generator seed to a specific value? (y/n) +n +# If yes, give the random generator seed (optional, default is 123): +789 +################################################################################ +# Specify parameters for NNTSSD.External_Testdata.predict_test_data: +################################################################################ +# Give the number of cores you want to use for mpirun (nnp-dataset): +4 +# Do you wish to fix the random generator seed to a specific value? (y/n) +n +# If yes, give the random generator seed (optional, default is 123): +789 +################################################################################ +# Specify parameters for NNTSSD.Validation.predict_validation_data: +################################################################################ +# Give the number of cores you want to use for mpirun (nnp-dataset): +4 +# Do you wish to fix the random generator seed to a specific value? (y/n) +n +# If yes, give the random generator seed (optional, default is 123): +789 diff --git a/tools/python/NNTSSD/example/example_README b/tools/python/NNTSSD/example/example_README new file mode 100644 index 000000000..940806a94 --- /dev/null +++ b/tools/python/NNTSSD/example/example_README @@ -0,0 +1,10 @@ +How to run an example: + +* build n2p2 (https://compphysvienna.github.io/n2p2/) by running '$ make static' in ~/n2p2/src/ +* run with '$ python3 ../source/NNTSSD.py' + +optional: +* plot learning curves/training performance with '$ python3 ../source/learning_curves.py' + + +INFO: For this example, 20 configurations from H2O_RPBE-D3 are used as original input training dataset. diff --git a/tools/python/NNTSSD/example/input.data b/tools/python/NNTSSD/example/input.data new file mode 100644 index 000000000..b70742706 --- /dev/null +++ b/tools/python/NNTSSD/example/input.data @@ -0,0 +1,2224 @@ + begin +comment written by RuNNerDecom +lattice 24.623508 0.000000 0.000000 +lattice 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0.000000000 0.014688099 -0.002133776 -0.011109509 +atom 23.134797998 17.370023640 17.326635511 O -0.251377170 0.000000000 -0.000268730 -0.000574628 -0.003573364 +atom 23.309537263 17.766571487 19.122904057 H 0.118237060 0.000000000 0.005638293 -0.001455468 -0.001185792 +atom 0.463480083 18.316502796 16.477809882 H 0.138644360 0.000000000 -0.009964426 -0.001923402 0.004419419 +atom 7.198349513 9.467121587 20.207068712 O -0.243428280 0.000000000 0.009787410 -0.009154114 0.016239349 +atom 7.841261053 8.491977165 21.715395794 H 0.127971510 0.000000000 -0.015877818 0.012471507 -0.019846659 +atom 5.641732350 10.325405299 20.696485297 H 0.130413120 0.000000000 0.003877775 0.000013170 0.004937889 +energy -4900.14395256 +charge 0.0000000000 + end diff --git a/tools/python/NNTSSD/example/input.nn b/tools/python/NNTSSD/example/input.nn new file mode 100644 index 000000000..b4a685b8e --- /dev/null +++ b/tools/python/NNTSSD/example/input.nn @@ -0,0 +1,187 @@ +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.3353824022135491E-01 +conv_energy 8.5167544626563699E+02 +conv_length 2.2798325235535355E+01 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +atom_energy H -0.45890771 # Free atom reference energy (H). +atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type (optional argument: shift parameter alpha). +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# ADDITIONAL SETTINGS FOR TRAINING +############################################################################### +# These keywords are solely used for training with nnp-train. +epochs 4 # Number of training epochs. +updater_type 1 # Weight update method (0 = Gradient Descent, 1 = Kalman filter). +parallel_mode 0 # Training parallelization used (0 = Parallel (rank 0 update), 1 = Parallel (all update)). +jacobian_mode 1 # Jacobian computation mode (0 = Summation to single gradient, 1 = Per-task summed gradient, 2 = Full Jacobian). +update_strategy 0 # Update strategy (0 = Combined, 1 = Per-element). +selection_mode 2 # Update candidate selection mode (0 = Random, 1 = Sort, 2 = Threshold). +task_batch_size_energy 1 # Number of energy update candidates prepared per task for each update (0 = Entire training set). +task_batch_size_force 1 # Number of force update candidates prepared per task for each update (0 = Entire training set). +memorize_symfunc_results # Keep symmetry function results in memory. +test_fraction 0 # Fraction of structures kept for testing. +force_weight 10.0 # Weight of force updates relative to energy updates. +short_energy_fraction 1.000 # Fraction of energy updates per epoch. +short_force_fraction 0.0041 # Fraction of force updates per epoch. +short_energy_error_threshold 0.00 # RMSE threshold for energy update candidates. +short_force_error_threshold 1.00 # RMSE threshold for force update candidates. +rmse_threshold_trials 3 # Maximum number of RMSE threshold trials. +#use_old_weights_short # Restart fitting with old weight parameters. +weights_min -1.0 # Minimum value for initial random weights. +weights_max 1.0 # Maximum value for initial random weights. +#precondition_weights # Precondition weights with initial energies. +#nguyen_widrow_weights_short # Initialize neural network weights according to Nguyen-Widrow scheme. +write_trainpoints 1 # Write energy comparison every this many epochs. +write_trainforces 1 # Write force comparison every this many epochs. +write_weights_epoch 1 # Write weights every this many epochs. +write_neuronstats 1 # Write neuron statistics every this many epochs. +write_trainlog # Write training log file. +#################### +# GRADIENT DESCENT # +#################### +# This section is only used if "updater_type" is "0". +gradient_type 1 # Gradient descent type (0 = Fixed step size, 1 = Adam). +gradient_eta 1.0E-5 # Fixed step size gradient descent parameter eta. +gradient_adam_eta 1.0E-3 # Adam parameter eta. +gradient_adam_beta1 0.9 # Adam parameter beta1. +gradient_adam_beta2 0.999 # Adam parameter beta2. +gradient_adam_epsilon 1.0E-8 # Adam parameter epsilon. +############################ +# KALMAN FILTER (STANDARD) # +############################ +# This section is only used if "updater_type" is "1". +kalman_type 0 # Kalman filter type (0 = Standard, 1 = Fading memory). +kalman_epsilon 1.0E-2 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +kalman_q0 0.01 # General Kalman filter parameter q0 ("large"). +kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +kalman_qmin 1.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +kalman_eta 0.01 # Standard Kalman filter parameter eta (0.001-1.0). +kalman_etatau 2.302 # Standard Kalman filter parameter etatau (2.302 => 1 order of magnitude per epoch). +kalman_etamax 1.0 # Standard Kalman filter parameter etamax (1.0+). +################################# +# KALMAN FILTER (FADING MEMORY) # +################################# +# This section is only used if "updater_type" is "1". +# The settings here enable an alternative Kalman filter variant and are NOT RECOMMENDED! +#kalman_type 1 # Kalman filter type (0 = Standard, 1 = Fading memory). +#kalman_epsilon 1.0E-1 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +#kalman_q0 0.00 # General Kalman filter parameter q0 ("large"). +#kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +#kalman_qmin 0.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +#kalman_lambda_short 0.96000 # Fading memory Kalman filter parameter lambda (forgetting factor 0.95-0.99). +#kalman_nue_short 0.99950 # Fading memory Kalman filter parameter nu (0.99-0.9995). + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +# NOTE: <> is optional! +#symfunction_short 3 < + +# Wide Angular symmetry function (type 9): +# NOTE: <> is optional! +#symfunction_short 9 < + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/example/predict_test_data/input.data b/tools/python/NNTSSD/example/predict_test_data/input.data new file mode 100644 index 000000000..b70742706 --- /dev/null +++ b/tools/python/NNTSSD/example/predict_test_data/input.data @@ -0,0 +1,2224 @@ + begin +comment written by RuNNerDecom +lattice 24.623508 0.000000 0.000000 +lattice 0.000000 24.623508 0.000000 +lattice 0.000000 0.000000 24.623508 +atom 3.735290838 2.327841157 3.501304856 O -0.226389360 0.000000000 -0.006530137 -0.002852459 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unit : Bohr +# Energy unit : Ha +# Reference method: RPBE-D3 +############################################################################### + +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.5521343547039809E+01 +conv_energy 2.4265748255366972E+02 +conv_length 5.8038448995319847E+00 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +#atom_energy H -0.45890771 # Free atom reference energy (H). +#atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type. +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +#symfunction_short 3 + +# Wide Angular symmetry function (type 9): +#symfunction_short 9 + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/example/predict_test_data/scaling.data b/tools/python/NNTSSD/example/predict_test_data/scaling.data new file mode 100644 index 000000000..2a9e0ad7a --- /dev/null +++ b/tools/python/NNTSSD/example/predict_test_data/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 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examples/nnp-train/H2O_RPBE-D3/scaling.data rename to tools/python/NNTSSD/example/scaling.data diff --git a/tools/python/NNTSSD/example/validation_data/input.data b/tools/python/NNTSSD/example/validation_data/input.data new file mode 100644 index 000000000..b70742706 --- /dev/null +++ b/tools/python/NNTSSD/example/validation_data/input.data @@ -0,0 +1,2224 @@ + begin +comment written by RuNNerDecom +lattice 24.623508 0.000000 0.000000 +lattice 0.000000 24.623508 0.000000 +lattice 0.000000 0.000000 24.623508 +atom 3.735290838 2.327841157 3.501304856 O -0.226389360 0.000000000 -0.006530137 -0.002852459 -0.027992554 +atom 5.246524320 3.393061301 3.627368536 H 0.120793430 0.000000000 0.004517488 -0.000517262 0.005052573 +atom 3.558676964 2.038939712 1.628158442 H 0.127225380 0.000000000 0.000095815 0.002211264 0.023427576 +atom 4.652261909 21.146799653 16.174455806 O -0.240350190 0.000000000 -0.000231046 -0.007150914 0.026944723 +atom 4.197045601 22.522898765 15.074691455 H 0.132518870 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-0.019846659 +atom 5.641732350 10.325405299 20.696485297 H 0.130413120 0.000000000 0.003877775 0.000013170 0.004937889 +energy -4900.14395256 +charge 0.0000000000 + end diff --git a/tools/python/NNTSSD/example/validation_data/input.nn b/tools/python/NNTSSD/example/validation_data/input.nn new file mode 100644 index 000000000..641276488 --- /dev/null +++ b/tools/python/NNTSSD/example/validation_data/input.nn @@ -0,0 +1,128 @@ +############################################################################### +# HDNNP for water H2O +############################################################################### +# Length unit : Bohr +# Energy unit : Ha +# Reference method: RPBE-D3 +############################################################################### + +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.5521343547039809E+01 +conv_energy 2.4265748255366972E+02 +conv_length 5.8038448995319847E+00 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +#atom_energy H -0.45890771 # Free atom reference energy (H). +#atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type. +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +#symfunction_short 3 + +# Wide Angular symmetry function (type 9): +#symfunction_short 9 + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/example/validation_data/scaling.data b/tools/python/NNTSSD/example/validation_data/scaling.data new file mode 100644 index 000000000..2a9e0ad7a --- /dev/null +++ b/tools/python/NNTSSD/example/validation_data/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 +# e_index sf_index sf_min sf_max sf_mean sf_sigma +######################################################################################################################### + 1 1 1.0882016636170764E+00 9.6166419119419064E+00 2.2691752247542194E+00 6.7883526611658462E-01 + 1 2 7.3274438904180561E-01 5.0028559321574191E+00 1.3272332317543580E+00 3.3936750181780473E-01 + 1 3 7.6010783783215696E-01 7.1427942966219815E+00 1.6470726712825305E+00 5.0771115927383836E-01 + 1 4 5.4842285884800812E-01 3.7661771168267726E+00 1.0163698211361718E+00 2.5362958053787776E-01 + 1 5 4.0080665126604625E-01 4.1469832401668629E+00 9.0925040981537897E-01 2.9758019277508319E-01 + 1 6 3.6209352253798227E-01 2.2678239402766054E+00 6.4931154122889623E-01 1.4835420345383032E-01 + 1 7 1.8919103878435897E-01 2.2292652677252804E+00 4.5693857051003817E-01 1.5976079618578123E-01 + 1 8 2.6704178695764313E-01 1.3208742362468955E+00 4.2395636902644862E-01 8.0492394978461931E-02 + 1 9 2.4513099752055156E-01 9.4751160662053002E-01 3.6244199023263673E-01 5.2993540556109331E-02 + 1 10 2.2248910067848982E-01 2.7596216013647377E+00 5.3891576898130766E-01 2.0137334230483950E-01 + 1 11 1.4743601726548086E-01 5.5599270746969276E-01 2.6773972195910817E-01 2.6188094566404998E-02 + 1 12 9.9110926426029380E-02 1.7265405335201480E+00 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6.3684016949344113E-03 6.6071626858835051E-03 + 1 23 3.3813162813665906E-02 9.1618560879938926E-01 8.1266384503339575E-02 5.7918502576695730E-02 + 1 24 4.1708500446352870E-04 1.5785966980407021E-01 4.6646981268568697E-03 9.8630700614506465E-03 + 1 25 7.3528900917695290E-04 5.9225627251013026E-02 3.7042174075139758E-03 3.3118079036492621E-03 + 1 26 8.9828333062972592E-03 1.9426085555380754E-01 2.4093377110646338E-02 1.0980657457661532E-02 + 1 27 2.1228022180417653E-04 8.7777813240869640E-03 2.0550705761547970E-03 5.8802103858137246E-04 + 2 1 1.5142595331454245E+00 1.0005711988559998E+01 2.6544664635087183E+00 6.7806617585688911E-01 + 2 2 4.4366445360926199E-01 4.6195409357987076E+00 9.6587051599896101E-01 3.3688559575009042E-01 + 2 3 1.1907810568758714E+00 7.5323544094345003E+00 2.0327396422723472E+00 5.0607867531004169E-01 + 2 4 2.7576036468694687E-01 3.3862131032504492E+00 6.5929732667024776E-01 2.5004687333979903E-01 + 2 5 8.0580777590695674E-01 4.5356481255168557E+00 1.2986230824577940E+00 2.9449908325462404E-01 + 2 6 1.0517053799863604E-01 1.8909877539194515E+00 3.0673921331641835E-01 1.4198497108573313E-01 + 2 7 5.6949141690859706E-01 2.6154328621607852E+00 8.4791273805289546E-01 1.5714071578589769E-01 + 2 8 2.3251646720171416E-02 9.3641034200657891E-01 1.1140979781150941E-01 6.9796654369842781E-02 + 2 9 5.1354161698115419E-01 1.8545341781448565E+00 7.2488398046527269E-01 9.8011511620611044E-02 + 2 10 1.1057465545812291E-01 2.9121456897811342E+00 4.7474421797982730E-01 2.3441807910092233E-01 + 2 11 3.5269317308496489E-01 1.0714592032613128E+00 5.3547944391821678E-01 4.5179661104166338E-02 + 2 12 3.0424313539726355E-02 2.5277642768509305E+00 3.1652845366685045E-01 2.1026891409654727E-01 + 2 13 1.5980022688828247E-01 6.6348817066386512E-01 3.7042498273566293E-01 3.0753700953611234E-02 + 2 14 2.7781847150922931E-03 2.3030057819082539E+00 1.7737800292869690E-01 1.8600239464755819E-01 + 2 15 9.5641036809349829E-03 3.9085233064570807E-01 1.5305059323200970E-01 2.7862233984302390E-02 + 2 16 3.7500170432292374E-06 2.0367068825281995E+00 5.4144316535640342E-02 1.4305857218443538E-01 + 2 17 2.4726232100491033E-03 3.4335400617385042E-01 1.6684597803376652E-02 2.1902951351570905E-02 + 2 18 1.7405672406959600E-05 5.6319316766205302E-02 9.5478184601751693E-04 3.3588039002222358E-03 + 2 19 5.4785372164647961E-02 3.0182597583971442E+00 2.0392031625072374E-01 2.0088721011517138E-01 + 2 20 1.3795234987637416E-03 4.9878800454061323E-01 1.2788265359933434E-02 3.1829452602194934E-02 + 2 21 6.6852772684814245E-03 2.6739582842775905E-01 3.0851859894574358E-02 1.7089886758420030E-02 + 2 22 1.7021399438214336E-02 1.4167796508898451E+00 7.6274174813506748E-02 9.2852504206357669E-02 + 2 23 1.9759831791959857E-02 4.0756378297923890E-01 4.8843503112397949E-02 2.5474332458885439E-02 + 2 24 5.2768632746659245E-04 2.3324050667069166E-01 7.2057238727819412E-03 1.4495435261027742E-02 + 2 25 1.1144879740881719E-05 3.5285772934088612E-02 4.2545240948261025E-04 2.0471375111485984E-03 + 2 26 1.6013752685265073E-02 8.2245409953473059E-01 5.0845479076508403E-02 5.2802834522172923E-02 + 2 27 3.9898424495541764E-03 7.8557031440100300E-01 3.6926675414383096E-02 5.0474458307624794E-02 + 2 28 4.0523818189746699E-05 9.8448068666705968E-02 1.2119235889230262E-03 5.7945700128174639E-03 + 2 29 6.0374649986214514E-03 9.9251766407842473E-02 1.6156539248049700E-02 5.5245068674135743E-03 + 2 30 2.9595491075765732E-03 1.5478537567691833E-01 1.1641055270110553E-02 8.9415193910804703E-03 diff --git a/tools/python/NNTSSD/source/NNTSSD.py b/tools/python/NNTSSD/source/NNTSSD.py new file mode 100755 index 000000000..14262d7fb --- /dev/null +++ b/tools/python/NNTSSD/source/NNTSSD.py @@ -0,0 +1,1014 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +NNTSSD: Neural Network Training Set Size Dependence +""" + +import os +import sys +import shutil +import numpy as np +import matplotlib.pyplot as plt +import file_input + +class Tools(): + """Tools for Neural Network Training Set Size Dependence. + """ + def __init__(self): + """In the moment, the class Tools() does not have any attributes. + + Still, it makes sense to define a class since it condenses NNTSSD functionalities and + for future purposes, it is likely that attributes will be added. + """ + + def create_training_datasets(self,set_size_ratios,n_sets_per_size,fix_random_seed_create,random_seed_create=123): + """Creates training datasets of different size from a given original dataset using the tool nnp-select. + + Parameters + ---------- + set_size_ratios : numpy.ndarray + One dimensional array; contains a list of ratios of the original training set size that are examined. + n_sets_per_size : int + Value, specifies how many sample sets per training size are considered. + fix_random_seed_create : logical + True if random seed for nnp-select shall be fixed, False otherwise. + random_seed_create : integer, optional + User-given fixed random number generator seed. Default is 123. + + Notes + ----- + Requirements + ``input.data`` : file + Contains original set of trainingdata. + ../../../../bin/nnp-select : executable program + Performs random selection of sets according to given ratio. + Outputs + ``Output`` : folder + It contains all of the following outputs. + ``Output/ratio*`` : folders + Its name tells the ratio * of current from original dataset. + ``Output/ratio*/ratio*_**`` : subfolders of the previous + Its name in addition tells the sample number ** of its ratio *. + ``Output/ratio*/ratio*.**/input.data`` : file + Contains new training dataset of specified size ratio. + ``Output/ratio*/ratio*_**/nnp-select.log`` : file + Log file created by running nnp-select. + """ + try: + os.path.isfile("input.data") + except: + sys.exit("ERROR: The file 'input.data' does not exist!") + try: + os.path.isfile("../../../../bin/nnp-select") + except: + sys.exit("ERROR: The executable program nnp-select does not exist in ~/n2p2/bin") + n_set_size_ratios = np.size(set_size_ratios) + print("\n***CREATING TRAINING DATASETS***************************************************") + print(" number of samples per training set size = ", n_sets_per_size) + print(" number of different training set sizes = ", n_set_size_ratios) + for ratios_counter in range(n_set_size_ratios): + current_ratio = set_size_ratios[ratios_counter] + print(" We are working with ratio {:3.2f}".format(current_ratio)) + ratio_folder = "ratio"+str("{:3.2f}".format(current_ratio)) + os.system("mkdir "+ratio_folder) + for sets_per_size_counter in range(1,n_sets_per_size+1): + if fix_random_seed_create: + random_seed = random_seed_create + else: + random_seed = int(np.random.randint(100,999,1)) + nnp_select = "../../../../bin/nnp-select random "+str("{:3.2f}".format(current_ratio))+" "+str(random_seed) + print(" ",nnp_select) + os.system(nnp_select) + os.chdir(ratio_folder) + set_folder = "ratio"+str("{:3.2f}".format(current_ratio))+"_set"+str(sets_per_size_counter) + os.system("mkdir "+set_folder) + os.chdir("../") + + os.path.isfile("output.data") + shutil.move("output.data",ratio_folder+"/"+set_folder+"/input.data") + shutil.move("nnp-select.log",ratio_folder+"/"+set_folder+"/nnp-select.log") + try: + shutil.rmtree("Output") + print(" INFO: Removed old 'Output' folder.") + except: + pass + os.system("mkdir "+"Output") + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + shutil.move(ratio_dir_string[ratio_dir_counter],"Output") + print("FINISHED creating datasets.") + return None + + def training_neural_network(self,nnp_train,n_epochs,test_fraction,write_submission_script_logical,fix_random_seed_train,random_seed_train=123,maximum_time=None): + """Trains the neural network with different existing datasets using the program nnp-train. + + Parameters + ---------- + nnp_train : string + Command for executing n2p2's nnp-train. + n_epochs : integer + Number of training epochs. + write_submission_script_logical : logical + If True, a job submission script for VSC is written in each of the folders 'Output/ratio*/ratio*_**/'. + If False, the command nnp_train is executed right away. + fix_random_seed_train : logical + True if random seed for nnp-train shall be fixed, False otherwise. + random_seed_train : integer, optional + User given fixed random number generator seed. Default is 123. + maximum_time: string, optional + User given maximum time required for executing the VSC job. Default is None. + + Notes + ----- + Requirements + ``Output/ratio*/ratio*_**`` : 3-layered directory structure + Created with the method create_training_datasets(). + ``Output/ratio*/ratio*_**/input.data`` : file + Contains the training datasets. + ../../../../bin/nnp-train : executable program + Performs training of neural network. + ``Output/ratio*/ratio*_**/input.nn`` : file + Specifies the training parameters. + ``Output/ratio*/ratio*_**/scaling.data`` : file + Contains symmetry function scaling data. + Outputs + ``Output/ratio*/ratio*_**/train.data`` : file + Dataset actually used for training. + ``Output/ratio*/ratio*_**/test.data`` : file + Dataset kept for testing. + ``Output/ratio*/ratio*_**/nnp-train.log.****`` : file + One or more log files from running nnp-train. + ``Output/ratio*/ratio*_**/learning-curve.out`` : file + Contains learning curve data, namely RMSE of energy and forces of train and test sets for each epoch. + """ + print("\n***TRAINING NEURAL NETWORK*****************************************************") + try: + os.chdir("Output") + except: + sys.exit("ERROR: The folder 'Output' does not exist!") + ratio_dir_string = np.sort(os.listdir()) + ratio_counter = 0 + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + ratio_counter += 1 + current_ratio = 0.01*int(''.join(filter(str.isdigit, ratio_dir_string[ratio_dir_counter]))) + print(" We are working with ratio {:3.2f}".format(current_ratio)) + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + os.chdir(set_dir_string[set_dir_counter]) + shutil.copy("../../../input.nn","input.nn") + if fix_random_seed_train: + random_seed = random_seed_train + else: + random_seed = int(np.random.randint(100,999,1)) + os.system("sed -i \"s/^{0:s} .*$/{0:s} {1:d}/g\" input.nn".format("random_seed", random_seed)) + os.system("sed -i \"s/^{0:s} .*$/{0:s} {1:d}/g\" input.nn".format("epochs", n_epochs)) + os.system("sed -i \"s/^{0:s} .*$/{0:s} {1:f}/g\" input.nn".format("test_fraction", test_fraction)) + shutil.copy("../../../scaling.data","scaling.data") + if write_submission_script_logical: + write_submission_script(command=nnp_train,job_name=set_dir_string[set_dir_counter]+"-training",time=maximum_time) + os.system("sbatch submit.slrm") #to submit the job to VSC + else: + print(" ",nnp_train) + os.system(nnp_train) + os.chdir("../") + os.chdir("../") + os.chdir("../") + print("FINISHED training with ",ratio_counter," different ratios.") + return None + + def analyse_learning_curves(self): + """Prepares analyse data from the learning curves obtained in training. + + Notes + ----- + Requirements + ``Output/ratio*/ratio*_**`` : 3-layered directory structure + Created with the method create_training_datasets(). + ``Output/ratio*/ratio*_**/learning-curve.out`` : file + Contains learning curve data, created with the method train_neural_network(). + Outputs + ``Output/ratio*/collect_data_min_***.out`` : file + Contains analysis of learning curve data of specific training size with respect to 'best' epoch, one file per optimization approach. + ``Output/analyse_data_min_***.out`` : file + Contains processed RMSE size dependence information for all dataset sizes with respect to 'best' epoch, one file per optimization approach. + ``Output/analyse_data_min_***_all.out`` : file + Contains processed RMSE size dependence information for all datasets with respect to 'best' epoch, one file per optimization approach. + """ + print("\n***ANALYSING LEARNING CURVES***************************************************") + if not os.path.isdir("Output"): + sys.exit("ERROR: The folder 'Output' does not exist!") + else: + os.chdir("Output") +# ANALYSE SIZE DEPENDENCE + epoch_min_arg = ["energy","force","comb"] + test_row_indices = [2,4,0] + for epoch_min_arg_counter in range(len(epoch_min_arg)): + current_epoch_min_arg = epoch_min_arg[epoch_min_arg_counter] + current_test_row_index = test_row_indices[epoch_min_arg_counter] + print(" Analysing data at epoch of minimum "+current_epoch_min_arg) + analyse_data = np.empty([0,10]) + analyse_data_all = np.empty([0,7]) + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + current_ratio = 0.01*int(''.join(filter(str.isdigit, ratio_dir_string[ratio_dir_counter]))) + print(" We are working with ratio {:3.2f}".format(current_ratio)) + collect_data = np.empty([0,7]) + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + sample_set_counter = 0 + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + sample_set_counter += 1 + os.chdir(set_dir_string[set_dir_counter]) + try: + learning_curve_data = np.genfromtxt("learning-curve.out") + number_of_epochs = int(learning_curve_data[-1,0]) + if (current_epoch_min_arg == "energy") or (current_epoch_min_arg == "force"): + cut_epoch_number = max(1,int(round(0.1*len(learning_curve_data[:,2])))) + index_epoch = 1+np.argmin(learning_curve_data[cut_epoch_number:,current_test_row_index]) #1: statt 20: + elif (current_epoch_min_arg == "comb"): + cut_epoch_number = max(1,int(round(0.1*len(learning_curve_data[:,2])))) + #Finde beste Epoche nach Punktesystem + index_epoch = 1+np.argmin(np.argsort(learning_curve_data[cut_epoch_number:,2])+np.argsort(learning_curve_data[cut_epoch_number:,4])) #1: statt 20: + collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sample_set_counter]),learning_curve_data[index_epoch,0:5],axis=0)]) + except: + print(" INFO: The file 'learning-curve.out' does not exist in "+ratio_dir_string[ratio_dir_counter]+"/"+set_dir_string[set_dir_counter]+"!") + os.chdir("../") + np.savetxt("collect_data_min_"+current_epoch_min_arg+".out",collect_data,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) + + mean_data = np.mean(collect_data,axis=0)[3:] + std_data = np.std(collect_data,axis=0)[3:] + wip_analyse_data = np.vstack([mean_data,std_data]) + wip_analyse_data = np.reshape(wip_analyse_data,8,order="F") + analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) + analyse_data_all = np.vstack([analyse_data_all,collect_data]) + os.chdir("../") + np.savetxt("analyse_data_min_"+current_epoch_min_arg+".out",analyse_data,fmt="%.18e", header='%15s%25s%25s%25s%25s%25s%25s%25s%25s%25s'%('ratio','epoch','mean_RMSE_E_train','std_RMSE_E_train','mean_RMSE_E_test','std_RMSE_E_test','mean_RMSE_F_train','std_RMSE_F_train','mean_RMSE_F_test','std_RMSE_F_test')) + np.savetxt("analyse_data_min_"+current_epoch_min_arg+"_all.out",analyse_data_all,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) + os.chdir("../") + print("FINISHED analysing learning curves.") + return None + + def plot_size_dependence(self): + """Plots the energy and force RMSE versus training set size. + + Firstly, this method carries out the original training set size from the file nnp-select.log + that has been created with create_training_datasets(). + Secondly, it plots the energy and force RMSE including their standard deviation versus the + selected training set sizes. + + Notes + ----- + Requirements + ``Output/analyse_data_min_force.out`` : file + Created with analyse_learning_curves(). + ``Output/analyse_data_min_energy.out`` : file + Created with analyse_learning_curves(). + ``Output/ratio*/ratio*_**/nnp-select.log`` : file + Created with create_training_datasets(). + Outputs + ``Output/int_Energy_RMSE_epoch_comparison.png`` : png picture + Shows train and test energy RMSE (and its standard deviation) versus training set size. + ``Output/int_Forces_RMSE_epoch_comparison.png`` : png picture + Shows train and test forces RMSE (and its standard deviation) versus training set size. + """ + print("\n***PLOTTING SIZE DEPENDENCE****************************************************") + if not os.path.isdir("Output"): + sys.exit("ERROR: The folder 'Output' does not exist!") + else: + os.chdir("Output") + if not os.path.isfile("analyse_data_min_energy.out"): + sys.exit("ERROR: The file 'analyse_data_min_energy.out' does not exist!") + if not os.path.isfile("analyse_data_min_force.out"): + sys.exit("ERROR: The file 'analyse_data_min_force.out' does not exist!") + ratio_dir_string = np.sort(os.listdir()) + n_total_configurations = 0 + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + os.chdir(set_dir_string[set_dir_counter]) + try: + nnp_select_log = open("nnp-select.log") + for line in nnp_select_log: + if line.startswith("Total"): + n_total_configurations = int(''.join(filter(str.isdigit, str(line)))) + except: + continue + os.chdir("../") + if not (n_total_configurations == 0): + break + os.chdir("../") + if not (n_total_configurations == 0): + break +# PLOT SIZE DEPENDENCE + print(" Plotting size dependence.") + analyse_data_E = np.genfromtxt("analyse_data_min_energy.out") + analyse_data_F = np.genfromtxt("analyse_data_min_force.out") + analyse_data_comb = np.genfromtxt("analyse_data_min_comb.out") + analyse_data_E_all = np.genfromtxt("analyse_data_min_energy_all.out") + analyse_data_F_all = np.genfromtxt("analyse_data_min_force_all.out") + analyse_data_comb_all = np.genfromtxt("analyse_data_min_comb_all.out") + training_set_sizes = analyse_data_E[:,0]*n_total_configurations + training_set_sizes_all = analyse_data_E_all[:,0]*n_total_configurations + plt.figure("Energy RMSE Set Size Dependence") + eh = 27.21138602e3 + plt.errorbar(training_set_sizes,eh*analyse_data_E[:,4],eh*analyse_data_E[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(4),label="Etest, epoch of min energy") + plt.errorbar(training_set_sizes,eh*analyse_data_E[:,2],eh*analyse_data_E[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(7),label="Etrain, epoch of min energy")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,eh*analyse_data_F[:,4],eh*analyse_data_F[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(8),label="Etest, epoch of min force") + plt.errorbar(training_set_sizes,eh*analyse_data_F[:,2],eh*analyse_data_F[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(11),label="Etrain, epoch of min force")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,4],eh*analyse_data_comb[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(0),label="Etest, epoch of min comb") + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,2],eh*analyse_data_comb[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(3),label="Etrain, epoch of min comb")[-1][0].set_linestyle('--') + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Energy RMSE Set Size Dependence (int.Testset)") + plt.xlabel("training set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("int_Energy_RMSE_epoch_comparison.png",dpi=300) + + plt.figure("Forces RMSE Set Size Dependence") + bohr = 27.21138602e3/5.2917721067e-1 + plt.errorbar(training_set_sizes,bohr*analyse_data_E[:,8],bohr*analyse_data_E[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(4),label="Ftest, epoch of min energy") + plt.errorbar(training_set_sizes,bohr*analyse_data_E[:,6],bohr*analyse_data_E[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(7),label="Ftrain, epoch of min energy")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,bohr*analyse_data_F[:,8],bohr*analyse_data_F[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(8),label="Ftest, epoch of min force") + plt.errorbar(training_set_sizes,bohr*analyse_data_F[:,6],bohr*analyse_data_F[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(11),label="Ftrain, epoch of min force")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,8],bohr*analyse_data_comb[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(0),label="Ftest, epoch of min comb") + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,6],bohr*analyse_data_comb[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(3),label="Ftrain, epoch of min comb")[-1][0].set_linestyle('--') + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Forces RMSE Set Size Dependence (int.Testset)") + plt.xlabel("training set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("int_Forces_RMSE_epoch_comparison.png",dpi=300) + + os.chdir("../") + print("FINISHED plotting size dependence.") + return None + +class Validation(): + """Tools for validating trained neural networks with user provided dataset. + """ + def __init__(self): + """In the moment, the class Validation() does not have any attributes. + + Still, it makes sense to define a class since it condenses NNTSSD functionalities and + for future purposes, it is likely that attributes will be added. + """ + def predict_validation_data(self,dataset_cores,fix_random_seed_validation,random_seed_validation=123): + """Predicts the energies and forces of a validation dataset for all trained neural networks + + Parameters + ---------- + fix_random_seed_validation : logical + True if random seed for nnp-dataset shall be fixed, False otherwise. + random_seed_validation : integer, optional + User given fixed random number generator seed. Default is 123. + + Notes + ----- + Requirements + ``Output/ratio*/ratio*_**`` : 3-layered directory structure + Created with the method NNTSSD.Tools.create_training_datasets(). + ``Output/ratio*/ratio*_**/weights.***.****`` : files + Created with the method NNTSSD.Tools.train_neural_network(). + ``validation_data`` : directory + Containing the following three files. + ``validation_data/input.data`` : file + Contains the dataset that shall be used for testing. + ``validation_data/input.nn`` : file + Specifies the parameters for nnp-dataset. + ``validation_data/scaling.data`` : file + Contains symmetry function scaling data. + Outputs + ``Output/ratio*/ratio*_**/validation`` : directory + Contains Output files created by nnp-dataset. + ``Output/validation_data_min_***.out`` : files + Contains processed RMSE size dependence information for all dataset sizes with respect to 'best' epoch, one file per optimization approach. + ``Output/validation_data_min_***_all.out`` : files + Contains RMSE size dependence information for all dataset samples of all sizes with respect to 'best' epoch, one file per optimization approach. + """ + print("\n***PREDICTING VALIDATION DATA************************************************") + try: + os.chdir("validation_data") #dieser Ordner enthält das File input.data mit den zurückgehaltenen Datensätzen, die zum Validieren verwendet werden. + os.chdir("../") + except: + sys.exit("ERROR: The folder 'validation_data' does not exist!") + os.chdir("Output") + epoch_min_arg = ["energy","force","comb"] + test_row_indices = [1,2,0] + for epoch_min_arg_counter in range(len(epoch_min_arg)): + current_epoch_min_arg = epoch_min_arg[epoch_min_arg_counter] + current_test_row_index = test_row_indices[epoch_min_arg_counter] + print(" Analysing data at epoch of minimum "+current_epoch_min_arg) + analyse_data_all = np.genfromtxt("analyse_data_min_"+current_epoch_min_arg+"_all.out") + overall_epoch_counter = 0 + validation_data = np.empty([0,5]) + validation_data_all = np.copy(analyse_data_all[:,0:5]) + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + current_ratio = 0.01*int(''.join(filter(str.isdigit, ratio_dir_string[ratio_dir_counter]))) + print(" We are working with ratio {:3.2f}".format(current_ratio)) + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + sample_set_counter = 0 + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + sample_set_counter += 1 + os.chdir(set_dir_string[set_dir_counter]) + if os.path.isdir("validation"): + shutil.rmtree("validation") +# print(" INFO: Removed old 'validation' folder.") + else: + pass + os.system("mkdir validation") + try: + shutil.copy("../../../validation_data/scaling.data","validation/scaling.data") + shutil.copy("../../../validation_data/input.data","validation/input.data") + shutil.copy("../../../validation_data/input.nn","validation/input.nn") + current_best_epoch = analyse_data_all[overall_epoch_counter,2] + shutil.copy("weights.001."+"{0:0=6d}".format(int(current_best_epoch))+".out","validation/weights.001.data") + shutil.copy("weights.008."+"{0:0=6d}".format(int(current_best_epoch))+".out","validation/weights.008.data") + os.chdir("validation") + if fix_random_seed_validation: + random_seed = random_seed_validation + else: + random_seed = int(np.random.randint(100,999,1)) + os.system("sed -i \"s/^{0:s} .*$/{0:s} {1:d}/g\" input.nn".format("random_seed", random_seed)) + os.system("mpirun -np "+str(dataset_cores)+" ../../../../../../../../bin/nnp-dataset 0") # >/dev/null + energy_comp = np.genfromtxt("energy.comp") + E_rmse = np.sqrt(np.mean((energy_comp[:,2]-energy_comp[:,3])**2)) + validation_data_all[overall_epoch_counter,3] = E_rmse + forces_comp = np.genfromtxt("forces.comp") + F_rmse = np.sqrt(np.mean((forces_comp[:,2]-forces_comp[:,3])**2)) + validation_data_all[overall_epoch_counter,4] = F_rmse +# np.savetxt("learning-curve-testdata.out",test_learning_curve,fmt="%.18e",header='%15s%25s%25s'%('epoch','rmse_Etest','rmse_Ftest')) +# overall_epoch_counter += 1 + os.chdir("../") + except: + print(" INFO: Something went worng!") + overall_epoch_counter += 1 + os.chdir("../") + os.chdir("../") + mean_data = np.mean(validation_data_all,axis=0)[3:] + std_data = np.std(validation_data_all,axis=0)[3:] + wip_validation_data = np.vstack([mean_data,std_data]) + wip_validation_data = np.reshape(wip_validation_data,4,order="F") + validation_data = np.vstack([validation_data,np.append(np.array([current_ratio]),wip_validation_data,axis=0)]) + np.savetxt("validation_data_min_"+current_epoch_min_arg+"_all.out",validation_data_all,fmt="%.18e",header='%15s%25s%25s%25s%25s'%('ratio','set_number','epoch','RMSE_E_validation','RMSE_F_validation')) + np.savetxt("validation_data_min_"+current_epoch_min_arg+".out",validation_data,fmt="%.18e", header='%15s%25s%25s%25s%25s'%('ratio','mean_RMSE_E_validation','std_RMSE_E_validation','mean_RMSE_F_validation','std_RMSE_F_validation')) + os.chdir("../") + print("FINISHED predicting validation data.") + return None + + def plot_validation_data(self): + """Plots the energy and force RMSE versus training set size. + + Firstly, this method carries out the original training set size from the file nnp-select.log + that has been created with create_training_datasets(). + Secondly, it plots the energy and force RMSE including their standard deviation versus the + selected training set sizes. + + Notes + ----- + Requirements + ``Output/analyse_data_min_***.out`` : files + Created with analyse_learning_curves(). + ``Output/validation_data_min_***.out`` : files + Created with predict_validation_data(). + ``Output/validation_data_min_***_all.out`` : files + Created with predict_validation_data(). + ``Output/ratio*/ratio*_**/nnp-select.log`` : file + Created with create_training_datasets(). + Outputs + ``Output/val_Energy_RMSE.png`` : png picture + Shows train and test energy RMSE (and its standard deviation) versus training set size. + ``Output/val_Energy_RMSE_best_E.png`` : png picture + As above, using epoch of minimum energy. + ``Output/val_Forces_RMSE.png`` : png picture + Shows train and test forces RMSE (and its standard deviation) versus training set size. + ``Output/val_Forces_RMSE_best_F.png`` : png picture + As above, using epoch of minimum forces. + """ + print("\n***PLOTTING TEST SIZE DEPENDENCE W/ VALIDATION DATA****************************") + if not os.path.isdir("Output"): + sys.exit("ERROR: The folder 'Output' does not exist!") + else: + os.chdir("Output") + if not os.path.isfile("analyse_data_min_energy.out"): + sys.exit("ERROR: The file 'analyse_data_min_energy.out' does not exist!") + if not os.path.isfile("analyse_data_min_force.out"): + sys.exit("ERROR: The file 'analyse_data_min_force.out' does not exist!") + ratio_dir_string = np.sort(os.listdir()) + n_total_configurations = 0 + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + os.chdir(set_dir_string[set_dir_counter]) + try: + nnp_select_log = open("nnp-select.log") + for line in nnp_select_log: + if line.startswith("Total"): + n_total_configurations = int(''.join(filter(str.isdigit, str(line)))) + except: + continue + os.chdir("../") + if not (n_total_configurations == 0): + break + os.chdir("../") + if not (n_total_configurations == 0): + break +# PLOT SIZE DEPENDENCE + print(" Plotting test size dependence.") + analyse_data_E = np.genfromtxt("analyse_data_min_energy.out") + analyse_data_F = np.genfromtxt("analyse_data_min_force.out") + analyse_data_comb = np.genfromtxt("analyse_data_min_comb.out") +# analyse_data_E_all = np.genfromtxt("analyse_data_min_energy_all.out") +# analyse_data_F_all = np.genfromtxt("analyse_data_min_force_all.out") +# analyse_data_comb_all = np.genfromtxt("analyse_data_min_comb_all.out") + + validation_data_comb_all = np.genfromtxt("validation_data_min_comb_all.out") + validation_data_E_all = np.genfromtxt("validation_data_min_energy_all.out") + validation_data_F_all = np.genfromtxt("validation_data_min_force_all.out") + + training_set_sizes = analyse_data_comb[:,0]*n_total_configurations + training_set_sizes_all = validation_data_comb_all[:,0]*n_total_configurations + + plt.figure("Energy RMSE comb") + eh = 27.21138602e3 + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,4],eh*analyse_data_comb[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt=",",markersize=3,color=plt.cm.tab20c(1),label="Etest, mean") + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,2],eh*analyse_data_comb[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt=",",markersize=3,color=plt.cm.tab20c(3),label="Etrain, mean")[-1][0].set_linestyle('--') + plt.plot(training_set_sizes_all,eh*validation_data_comb_all[:,3],'*',color=plt.cm.tab20c(0),label="Validation data") + plt.grid(b=True, which='major',color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Validation Energy RMSE SSD, combined best epoch") + plt.xlabel("training set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("val_Energy_RMSE.png",dpi=300) + + plt.figure("Forces RMSE comb") + bohr = 27.21138602e3/5.2917721067e-1 + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,8],bohr*analyse_data_comb[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt=",",markersize=3,color=plt.cm.tab20c(1),label="Ftest, mean") + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,6],bohr*analyse_data_comb[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt=",",markersize=3,color=plt.cm.tab20c(3),label="Ftrain, mean")[-1][0].set_linestyle('--') + plt.plot(training_set_sizes_all,bohr*validation_data_comb_all[:,4],'*',color=plt.cm.tab20c(0),label="Validation data") + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Validation Forces RMSE SSD, combined best epoch") + plt.xlabel("training set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("val_Forces_RMSE.png",dpi=300) + + plt.figure("Energy RMSE best E") + plt.errorbar(training_set_sizes,eh*analyse_data_E[:,4],eh*analyse_data_E[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt=",",markersize=3,color=plt.cm.tab20c(5),label="Etest, mean") + plt.errorbar(training_set_sizes,eh*analyse_data_E[:,2],eh*analyse_data_E[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt=",",markersize=3,color=plt.cm.tab20c(7),label="Etrain, mean")[-1][0].set_linestyle('--') + plt.plot(training_set_sizes_all,eh*validation_data_E_all[:,3],'*',color=plt.cm.tab20c(4),label="Validation data") + plt.grid(b=True, which='major',color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Validation Energy RMSE SSD, best energy epoch") + plt.xlabel("training set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("val_Energy_RMSE_best_E.png",dpi=300) + + plt.figure("Forces RMSE best F") + plt.errorbar(training_set_sizes,bohr*analyse_data_F[:,8],bohr*analyse_data_F[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt=",",markersize=3,color=plt.cm.tab20c(9),label="Ftest, mean") + plt.errorbar(training_set_sizes,bohr*analyse_data_F[:,6],bohr*analyse_data_F[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt=",",markersize=3,color=plt.cm.tab20c(11),label="Ftrain, mean")[-1][0].set_linestyle('--') + plt.plot(training_set_sizes_all,bohr*validation_data_F_all[:,4],'*',color=plt.cm.tab20c(8),label="Validation data") + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Validation Forces RMSE SSD, best forces epoch") + plt.xlabel("training set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("val_Forces_RMSE_best_F.png",dpi=300) + + os.chdir("../") + print("FINISHED plotting test size dependence.") + return None + +class External_Testdata(): + """Tools for testing trained neural networks with user provided dataset. + """ + def __init__(self): + """In the moment, the class External_Testdata() does not have any attributes. + + Still, it makes sense to define a class since it condenses NNTSSD functionalities and + for future purposes, it is likely that attributes will be added. + """ + + def predict_test_data(self,dataset_cores,fix_random_seed_predict,random_seed_predict=123): + """Predicts the energies and forces of a test dataset for all trained neural networks. + + Parameters + ---------- + fix_random_seed_predict : logical + True if random seed for nnp-dataset shall be fixed, False otherwise. + random_seed_predict : integer, optional + User given fixed random number generator seed. Default is 123. + + Notes + ----- + Requirements + ``Output/ratio*/ratio*_**`` : 3-layered directory structure + Created with the method NNTSSD.create_training_datasets(). + ``Output/ratio*/ratio*_**/weights.***.****`` : files + Created with the method NNTSSD.train_neural_network(). + ``predict_test_data`` : directory + Containing the following three files. + ``predit_test_data/input.data`` : file + Contains the dataset that shall be used for testing. + ``predict_test_data/input.nn`` : file + Specifies the parameters for nnp-dataset. + ``predict_test_dataset/scaling.data`` : file + Contains symmetry function scaling data. + Outputs + ``Output/ratio*/ratio*_**/predict_testdata`` : directory + Contains Output files created by nnp-dataset and the following file. + ``Output/ratio*/ratio*_**/predict_testdata/learning-curve-testdata.out`` : file + Contains learning curve data for testset. + """ + print("\n***PREDICTING EXTERNAL TESTDATA************************************************") + try: + os.chdir("predict_test_data") #dieser Ordner enthält das File test.data mit den zurückgehaltenen Datensätzen, die zum Testen verwendet werden. + os.chdir("../") + except: + sys.exit("ERROR: The folder 'predict_test_data' does not exist!") + os.chdir("Output") + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + current_ratio = 0.01*int(''.join(filter(str.isdigit, ratio_dir_string[ratio_dir_counter]))) + print(" We are working with ratio {:3.2f}".format(current_ratio)) + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + sample_set_counter = 0 + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + sample_set_counter += 1 + os.chdir(set_dir_string[set_dir_counter]) + try: + shutil.rmtree("predict_testdata") + print(" INFO: Removed old 'predict_testdata' folder.") + except: + pass + os.system("mkdir predict_testdata") + try: + shutil.copy("../../../predict_test_data/scaling.data","predict_testdata/scaling.data") + shutil.copy("../../../predict_test_data/input.data","predict_testdata/input.data") + shutil.copy("../../../predict_test_data/input.nn","predict_testdata/input.nn") + listdir = np.sort(os.listdir()) + weightslist_001 = np.sort(np.array([item for item in listdir if item.startswith('weights.001.')])) + weightslist_008 = np.sort(np.array([item for item in listdir if item.startswith('weights.008.')])) + test_learning_curve = np.empty([len(weightslist_001),3]) + for current_epoch in range(len(weightslist_001)): + test_learning_curve[current_epoch,0] = current_epoch + shutil.copy("weights.001."+"{0:0=6d}".format(current_epoch)+".out","predict_testdata/weights.001.data") + shutil.copy("weights.008."+"{0:0=6d}".format(current_epoch)+".out","predict_testdata/weights.008.data") + os.chdir("predict_testdata") + if fix_random_seed_predict: + random_seed = random_seed_predict + else: + random_seed = int(np.random.randint(100,999,1)) + os.system("sed -i \"s/^{0:s} .*$/{0:s} {1:d}/g\" input.nn".format("random_seed", random_seed)) + os.system("mpirun -np "+str(dataset_cores)+" ../../../../../../../../bin/nnp-dataset 0") # >/dev/null + energy_comp = np.genfromtxt("energy.comp") + E_rmse = np.sqrt(np.mean((energy_comp[:,2]-energy_comp[:,3])**2)) + test_learning_curve[current_epoch,1] = E_rmse + forces_comp = np.genfromtxt("forces.comp") + F_rmse = np.sqrt(np.mean((forces_comp[:,2]-forces_comp[:,3])**2)) + test_learning_curve[current_epoch,2] = F_rmse + np.savetxt("learning-curve-testdata.out",test_learning_curve,fmt="%.18e",header='%15s%25s%25s'%('epoch','rmse_Etest','rmse_Ftest')) + os.chdir("../") + except: + print(" INFO: Something went worng!") + os.chdir("../") + os.chdir("../") + os.chdir("../") + print("FINISHED predicting test data.") + return None + + def analyse_learning_curves(self): + """Prepares analyse data from the learning curves obtained in training neural network and predicting testdata. + + Notes + ----- + Requirements + ``Output/ratio*/ratio*_**`` : 3-layered directory structure + Created with the method NNTSSD.create_training_datasets(). + ``Output/ratio*/ratio*_**/learning-curve.out`` : file + Contains learning curve data, created with the method NNTSSD.create_training_datasets(). + ``Output/ratio*/ratio*_**/predict_testdata/learning-curve-testdata.out`` : file + Created with the method predict_test_data(). + Outputs + ``Output/ratio*/ext_collect_data_min_***.out`` : files + Contains analysis of learning curve data of specific training size with respect to 'best' epoch, one file per optimization approach. + ``Output/ext_analyse_data_min_***.out`` : files + Contains processed RMSE size dependence information for all dataset sizes with respect to 'best' epoch, one file per optimization approach. + ``Output/ext_analyse_data_min_***_all.out`` : files + Contains RMSE size dependence information for all dataset samples of all sizes with respect to 'best' epoch, one file per optimization approach. + """ + print("\n***ANALYSING LEARNING CURVES W/ EXTERNAL TESTDATA******************************") + if not os.path.isdir("Output"): + sys.exit("ERROR: The folder 'Output' does not exist!") + else: + os.chdir("Output") + epoch_min_arg = ["energy","force","comb"] + test_row_indices = [1,2,0] + for epoch_min_arg_counter in range(len(epoch_min_arg)): + current_epoch_min_arg = epoch_min_arg[epoch_min_arg_counter] + current_test_row_index = test_row_indices[epoch_min_arg_counter] + print(" Analysing data at epoch of minimum "+current_epoch_min_arg) + analyse_data = np.empty([0,10]) + analyse_data_all = np.empty([0,7]) + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + current_ratio = 0.01*int(''.join(filter(str.isdigit, ratio_dir_string[ratio_dir_counter]))) + print(" We are working with ratio {:3.2f}".format(current_ratio)) + collect_data = np.empty([0,7]) + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + sample_set_counter = 0 + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + sample_set_counter += 1 + os.chdir(set_dir_string[set_dir_counter]) + try: + learning_curve_testdata = np.genfromtxt("predict_testdata/learning-curve-testdata.out") + learning_curve_data = np.genfromtxt("learning-curve.out") + number_of_epochs = int(learning_curve_data[-1,0]) + if (current_epoch_min_arg == "energy") or (current_epoch_min_arg == "force"): + index_epoch = 1+np.argmin(learning_curve_testdata[1:,current_test_row_index]) + elif (current_epoch_min_arg == "comb"): + #Finde beste Epoche nach Punktesystem + index_epoch = 1+np.argmin(np.argsort(learning_curve_testdata[1:,1])+np.argsort(learning_curve_testdata[1:,2])) + wip_collect_data = np.array([learning_curve_testdata[index_epoch,0],learning_curve_data[index_epoch,1],learning_curve_testdata[index_epoch,1],learning_curve_data[index_epoch,3],learning_curve_testdata[index_epoch,2]]) + collect_data = np.vstack([collect_data,np.append(np.array([current_ratio,sample_set_counter]),wip_collect_data,axis=0)]) + except: + print(" INFO: Something went wrong!") + os.chdir("../") + np.savetxt("ext_collect_data_min_"+current_epoch_min_arg+".out",collect_data,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) + + mean_data = np.mean(collect_data,axis=0)[3:] + std_data = np.std(collect_data,axis=0)[3:] + wip_analyse_data = np.vstack([mean_data,std_data]) + wip_analyse_data = np.reshape(wip_analyse_data,8,order="F") + analyse_data = np.vstack([analyse_data,np.append(np.take(collect_data,[0,2]),wip_analyse_data,axis=0)]) + analyse_data_all = np.vstack([analyse_data_all,collect_data]) + os.chdir("../") + np.savetxt("ext_analyse_data_min_"+current_epoch_min_arg+".out",analyse_data,fmt="%.18e", header='%15s%25s%25s%25s%25s%25s%25s%25s%25s%25s'%('ratio','epoch','mean_RMSE_E_train','std_RMSE_E_train','mean_RMSE_E_test','std_RMSE_E_test','mean_RMSE_F_train','std_RMSE_F_train','mean_RMSE_F_test','std_RMSE_F_test')) + np.savetxt("ext_analyse_data_min_"+current_epoch_min_arg+"_all.out",analyse_data_all,fmt="%.18e",header='%15s%25s%25s%25s%25s%25s%25s'%('ratio','set_number','epoch','RMSE_E_train','RMSE_E_test','RMSE_F_train','RMSE_F_test')) + os.chdir("../") + print("FINISHED analysing learning curves.") + return None + + def plot_test_size_dependence(self): + """Plots the energy and force RMSE versus training set size. + + Firstly, this method carries out the original training set size from the file nnp-select.log + that has been created with create_training_datasets(). + Secondly, it plots the energy and force RMSE including their standard deviation versus the + selected training set sizes. + + Notes + ----- + Requirements + ``Output/analyse_data_min_force.out`` : file + Created with analyse_learning_curves(). + ``Output/analyse_data_min_energy.out`` : file + Created with analyse_learning_curves(). + ``Output/ratio*/ratio*_**/nnp-select.log`` : file + Created with create_training_datasets(). + Outputs + ``Output/ext_Energy_RMSE.png`` : png picture + Shows train and test energy RMSE (and its standard deviation) versus training set size at combined best epoch. + ``Output/ext_Energy_RMSE_epoch_compare.png`` : png picture + As above, using three different epochs (minimum force, energy, combined best). + ``Output/ext_Forces_RMSE.png`` : png picture + Shows train and test forces RMSE (and its standard deviation) versus training set size. + ``Output/ext_Forces_RMSE_epoch_compare.png`` : png picture + As above, using three different epochs (minimum force, energy, combined best). + """ + print("\n***PLOTTING TEST SIZE DEPENDENCE W/ EXTERNAL TESTDATA**************************") + if not os.path.isdir("Output"): + sys.exit("ERROR: The folder 'Output' does not exist!") + else: + os.chdir("Output") + if not os.path.isfile("ext_analyse_data_min_energy.out"): + sys.exit("ERROR: The file 'analyse_data_min_energy.out' does not exist!") + if not os.path.isfile("ext_analyse_data_min_force.out"): + sys.exit("ERROR: The file 'analyse_data_min_force.out' does not exist!") + ratio_dir_string = np.sort(os.listdir()) + n_total_configurations = 0 + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + os.chdir(set_dir_string[set_dir_counter]) + try: + nnp_select_log = open("nnp-select.log") + for line in nnp_select_log: + if line.startswith("Total"): + n_total_configurations = int(''.join(filter(str.isdigit, str(line)))) + except: + continue + os.chdir("../") + if not (n_total_configurations == 0): + break + os.chdir("../") + if not (n_total_configurations == 0): + break +# PLOT SIZE DEPENDENCE + print(" Plotting test size dependence.") + analyse_data_E = np.genfromtxt("ext_analyse_data_min_energy.out") + analyse_data_F = np.genfromtxt("ext_analyse_data_min_force.out") + analyse_data_comb = np.genfromtxt("ext_analyse_data_min_comb.out") + analyse_data_E_all = np.genfromtxt("ext_analyse_data_min_energy_all.out") + analyse_data_F_all = np.genfromtxt("ext_analyse_data_min_force_all.out") + analyse_data_comb_all = np.genfromtxt("ext_analyse_data_min_comb_all.out") + training_set_sizes = analyse_data_comb[:,0]*n_total_configurations + training_set_sizes_all = analyse_data_comb_all[:,0]*n_total_configurations + + plt.figure("Energy RMSE SSD") + eh = 27.21138602e3 + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,4],eh*analyse_data_comb[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt=",",markersize=3,color=plt.cm.tab20c(1),label="Etest, mean") + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,2],eh*analyse_data_comb[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt=",",markersize=3,color=plt.cm.tab20c(3),label="Etrain, mean")[-1][0].set_linestyle('--') + plt.plot(training_set_sizes_all,eh*analyse_data_comb_all[:,4],'*',color=plt.cm.tab20c(0),label="Etest, sample results") + plt.grid(b=True, which='major',color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Energy RMSE SSD, combined best epoch (ext.Testset)") + plt.xlabel("training set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("ext_Energy_RMSE.png",dpi=300) + + plt.figure("Forces RMSE SSD") + bohr = 27.21138602e3/5.2917721067e-1 + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,8],bohr*analyse_data_comb[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt=",",markersize=3,color=plt.cm.tab20c(1),label="Ftest, mean") + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,6],bohr*analyse_data_comb[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt=",",markersize=3,color=plt.cm.tab20c(3),label="Ftrain, mean")[-1][0].set_linestyle('--') + plt.plot(training_set_sizes_all,bohr*analyse_data_comb_all[:,6],'*',color=plt.cm.tab20c(0),label="Ftest, sample results") + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Forces RMSE SSD, combined best epoch (ext.Testset)") + plt.xlabel("training set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("ext_Forces_RMSE.png",dpi=300) + + plt.figure("Energy RMSE epoch comparison") + eh = 27.21138602e3 + plt.errorbar(training_set_sizes,eh*analyse_data_E[:,4],eh*analyse_data_E[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(4),label="Etest, epoch of min energy") + plt.errorbar(training_set_sizes,eh*analyse_data_E[:,2],eh*analyse_data_E[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(7),label="Etrain, epoch of min energy")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,eh*analyse_data_F[:,4],eh*analyse_data_F[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(8),label="Etest, epoch of min force") + plt.errorbar(training_set_sizes,eh*analyse_data_F[:,2],eh*analyse_data_F[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(11),label="Etrain, epoch of min force")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,4],eh*analyse_data_comb[:,5],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(0),label="Etest, epoch of min comb") + plt.errorbar(training_set_sizes,eh*analyse_data_comb[:,2],eh*analyse_data_comb[:,3],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(3),label="Etrain, epoch of min comb")[-1][0].set_linestyle('--') + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Energy RMSE epoch comparison (ext.Testset)") + plt.xlabel("training set size") + plt.ylabel(r"Energy RMSE (meV/atom)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("ext_Energy_RMSE_epoch_compare.png",dpi=300) + + plt.figure("Forces RMSE epoch comparison") + bohr = 27.21138602e3/5.2917721067e-1 + plt.errorbar(training_set_sizes,bohr*analyse_data_E[:,8],bohr*analyse_data_E[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(4),label="Ftest, epoch of min energy") + plt.errorbar(training_set_sizes,bohr*analyse_data_E[:,6],bohr*analyse_data_E[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(7),label="Ftrain, epoch of min energy")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,bohr*analyse_data_F[:,8],bohr*analyse_data_F[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(8),label="Ftest, epoch of min force") + plt.errorbar(training_set_sizes,bohr*analyse_data_F[:,6],bohr*analyse_data_F[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(11),label="Ftrain, epoch of min force")[-1][0].set_linestyle('--') + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,8],bohr*analyse_data_comb[:,9],errorevery=1,linewidth=1,capsize=2,ls="-",fmt="o",markersize=3,color=plt.cm.tab20c(0),label="Ftest, epoch of min comb") + plt.errorbar(training_set_sizes,bohr*analyse_data_comb[:,6],bohr*analyse_data_comb[:,7],errorevery=1,linewidth=1,capsize=2,ls="--",fmt="o",markersize=3,color=plt.cm.tab20c(3),label="Ftrain, epoch of min comb")[-1][0].set_linestyle('--') + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.yscale('log') + plt.xscale('log') + plt.title("Forces RMSE epoch comparison (ext.Testset)") + plt.xlabel("training set size") + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("ext_Forces_RMSE_epoch_compare.png",dpi=300) + + os.chdir("../") + print("FINISHED plotting test size dependence.") + return None + +def perform_NNTSSD(): + """This function performs NNTSSD methods according to user-given specifications. + + Firstly, it reads the NNTSSD parameters either from the file 'NNTSSD_input.dat' or, + if not successful, from interactive user input. + Secondly, it performs a user-given selection of the NNTSSD methods + Tools.create_training_datasets(), Tools.training_neural_network(), Tools.analyse_learning_curves(), Tools.plot_size_dependence(), + External_Testdata.predict_test_data(), External_Testdata.analyse_learning_curves() and External_Testdata.plot_test_size_dependence(). + """ + print("**********************************************************************") + print("NNTSSD - Tools for Neural Network Training Set Size Dependence") + print("**********************************************************************") + + try: + create_logical,train_logical,analyse_logical,plot_logical,\ + External_test_logical,External_analyse_logical,External_plot_logical,\ + Validation_predict_logical,Validation_plot_logical,\ + set_size_ratios,n_sets_per_size,fix_random_seed_create,random_seed_create,\ + n_epochs,test_fraction,mpirun_cores,write_submission_script_logical,maximum_time,fix_random_seed_train,random_seed_train,\ + dataset_cores,fix_random_seed_predict,random_seed_predict,\ + validation_dataset_cores,fix_random_seed_validation,random_seed_validation\ + = file_input.read_parameters_from_file() + except: + sys.exit("ERROR: Something went wrong with your NNTSSD input parameters.") + + nnp_train = "mpirun -np "+str(mpirun_cores)+" ../../../../../../../bin/nnp-train" + + print("Performing the following NNTSSD steps:") + print(" ",create_logical, "\tTools\t Create training datasets") + print(" ",train_logical, "\tTools\t Training neural network") + print(" ",analyse_logical, "\tTools\t Analyse learning curves") + print(" ",plot_logical, "\tTools\t Plot size dependence") + print(" ",External_test_logical, "\tExternal Testset\t Predicting external testset") + print(" ",External_analyse_logical, "\tExternal Testset\t Analyse learning curves wrt external testset") + print(" ",External_plot_logical, "\tExternal Testset\t Plot size dependence wrt external testset") + print(" ",Validation_predict_logical, "\tValidation\t Predicting and analysing validation dataset") + print(" ",Validation_plot_logical, "\tValidation\t Plot size dependence wrt validation dataset") + + myNNTSSD = Tools() + myExternal_Testdata = External_Testdata() + myValidation = Validation() + + if create_logical: + myNNTSSD.create_training_datasets(set_size_ratios,n_sets_per_size,fix_random_seed_create,random_seed_create) + if train_logical: + myNNTSSD.training_neural_network(nnp_train,n_epochs,test_fraction,write_submission_script_logical,fix_random_seed_train,random_seed_train,maximum_time) + if analyse_logical: + myNNTSSD.analyse_learning_curves() + if plot_logical: + myNNTSSD.plot_size_dependence() + if External_test_logical: + myExternal_Testdata.predict_test_data(dataset_cores,fix_random_seed_predict,random_seed_predict) + if External_analyse_logical: + myExternal_Testdata.analyse_learning_curves() + if External_plot_logical: + myExternal_Testdata.plot_test_size_dependence() + if Validation_predict_logical: + myValidation.predict_validation_data(validation_dataset_cores,fix_random_seed_validation,random_seed_validation) + if Validation_plot_logical: + myValidation.plot_validation_data() + return None + +def write_submission_script(command,job_name="",n_nodes=1,n_tasks_per_node=16,n_tasks_per_core=1,time=None): + """Writes a submission script 'submit.slrm' for running a job command on VSC (Vienna Scientific Cluster). + + Parameters + ---------- + command : string + Job command for VSC. + job_name : string + Name of the job. Default is none. + n_nodes : integer + Number of nodes requested (16 cores per node available). Default is 1. + n_tasks_per_node : integer + Number of processes run in parallel on a single node. Default is 16. + n_tasks_per_core : integer + Number of tasks a single core should work on. Default is 1. + time : string, optional + Maximum time required for exectuing the job; eg. time='08:00:00' for eight hours. + """ + submission_script = open("submit.slrm","w") + submission_script.write("#!/bin/bash\n#\n") + submission_script.write("#SBATCH -J "+job_name+"\n") + submission_script.write("#SBATCH -N "+str(n_nodes)+"\n") + submission_script.write("#SBATCH --ntasks-per-node="+str(n_tasks_per_node)+"\n") + submission_script.write("#SBATCH --ntasks-per-core="+str(n_tasks_per_core)+"\n") + if not time==None: + submission_script.write("#SBATCH --time="+time+"\n") + submission_script.write("\n"+command) + submission_script.close() + +def main(): + pass +if __name__=="__main__": + perform_NNTSSD() diff --git a/tools/python/NNTSSD/source/VSC_submission_script_for_NNTSSD.py b/tools/python/NNTSSD/source/VSC_submission_script_for_NNTSSD.py new file mode 100644 index 000000000..4e1cb81c4 --- /dev/null +++ b/tools/python/NNTSSD/source/VSC_submission_script_for_NNTSSD.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 19 14:38:55 2019 + +@author: mr +""" +import os + +def VSC_run_NNTSSD(): + write_submission_script("python3 ../source/NNTSSD.py",job_name="NNTSSD") + os.system("sbatch submit.slrm") #to submit the job to VSC + +def write_submission_script(command,job_name="",n_nodes=1,n_tasks_per_node=16,n_tasks_per_core=1,time=None): + """Writes a submission script 'submit.slrm' for running a job command on VSC (Vienna Scientific Cluster). + + Parameters + ---------- + command : string + Job command for VSC. + job_name : string + Name of the job. Default is none. + n_nodes : integer + Number of nodes requested (16 cores per node available). Default is 1. + n_tasks_per_node : integer + Number of processes run in parallel on a single node. Default is 16. + n_tasks_per_core : integer + Number of tasks a single core should work on. Default is 1. + time : string, optional + Maximum time required for exectuing the job; eg. time='08:00:00' for eight hours. + """ + submission_script = open("submit.slrm","w") + submission_script.write("#!/bin/bash\n#\n") + submission_script.write("#SBATCH -J "+job_name+"\n") + submission_script.write("#SBATCH -N "+str(n_nodes)+"\n") + submission_script.write("#SBATCH --ntasks-per-node="+str(n_tasks_per_node)+"\n") + submission_script.write("#SBATCH --ntasks-per-core="+str(n_tasks_per_core)+"\n") + if not time==None: + submission_script.write("#SBATCH --time="+time+"\n") + submission_script.write("#SBATCH --mail-type=BEGIN,END"+"\n") + submission_script.write("#SBATCH --mail-user="+"\n") + submission_script.write("\n"+command) + submission_script.close() + +def main(): + pass +if __name__=="__main__": + VSC_run_NNTSSD() diff --git a/tools/python/NNTSSD/source/file_input.py b/tools/python/NNTSSD/source/file_input.py new file mode 100644 index 000000000..2f7c4c182 --- /dev/null +++ b/tools/python/NNTSSD/source/file_input.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Tools for reading the NNTSSD parameters from file 'NNTSSD_input.dat'. +""" + +import numpy as np +import sys + +def convert_yes_no_to_logical(yn_input): + """This function converts 'y' and 'n' into the logicals True or False. + + Parameters + ---------- + yn_input : string + Should contain 'y' or 'n'. + + Returns + ------- + logical : logical + True if the input string has been 'y'. + False if the input string has been 'n'. + + Raises + ------ + ValueError + If input string does not contain either 'y' or 'n'. + """ + if yn_input == "y": + logical = True + elif yn_input == "n": + logical = False + else: + raise ValueError("Please input 'y' or 'n' for a (y/n) question.") +# sys.exit("ERROR: Please input 'y' or 'n' for a (y/n) question.") + return logical + +def read_parameters_from_file(): + """This function reads the NNTSSD parameters from the file 'NNTSSD_input.dat'. + + Requirements + ------------ + 'NNTSSD_input.dat' : file + Contains user-given specifications on training set size parameters and NNTSSD steps. + + Returns + ------- + create_logical : logical + True if the function NNTSSD.Tools.create_training_datasets() shall be performed. False otherwise. + train_logical : logical + True if the function NNTSSD.Tools.training_neural_network() shall be performed. False otherwise. + analyse_logical : logical + True if the function NNTSSD.Tools.analyse_learning_curves() shall be performed. False otherwise. + plot_logical : logical + True if the function NNTSSD.Tools.plot_size_dependence() shall be performed. False otherwise. + set_size_ratios : numpy.ndarray + One dimensional array; contains a list of ratios of the original training set size that are examined. + n_sets_per_size : int + Value, specifies how many sample sets per training size are considered. + fix_random_seed_create : logical + True if random seed for nnp-select shall be fixed, False otherwise. + random_seed_create : integer, optional + User-given fixed random number generator seed. Default is 123. + write_submission_script_logical : logical + If True, a job submission script for VSC is written in each of the folders 'Output/ratio*/ratio*_**/'. + If False, the command nnp_train is executed right away. + fix_random_seed_train : logical + True if random seed for nnp-train shall be fixed, False otherwise. + random_seed_train : integer, optional + User given fixed random number generator seed. Default is 123. + maximum_time: string, optional + User given maximum time required for executing the VSC job. Default is None. + mpirun_cores : integer + Number of cores that shall be used for executing the training. + n_epochs : integer + Number of training epochs. + """ + content = [] + user_input = open("NNTSSD_input.dat") + for line in user_input: + if not line.strip().startswith("#"): + content.append(line.rstrip().split()) + create_logical = convert_yes_no_to_logical(content[0][0]) + train_logical = convert_yes_no_to_logical(content[0][1]) + analyse_logical = convert_yes_no_to_logical(content[0][2]) + plot_logical = convert_yes_no_to_logical(content[0][3]) + External_test_logical = convert_yes_no_to_logical(content[1][0]) + External_analyse_logical = convert_yes_no_to_logical(content[1][1]) + External_plot_logical = convert_yes_no_to_logical(content[1][2]) + Validation_predict_logical = convert_yes_no_to_logical(content[2][0]) + Validation_plot_logical = convert_yes_no_to_logical(content[2][1]) + if create_logical: + set_size_ratios = np.array([float(x) for x in content[3]]) + if len(set_size_ratios) == 0: + set_size_ratios = np.arange(float(content[4][0]),float(content[4][1])+float(content[4][2]),float(content[4][2])) + n_sets_per_size = abs(int(content[5][0])) + fix_random_seed_create = convert_yes_no_to_logical(content[6][0]) + if fix_random_seed_create: + try: + random_seed_create = abs(int(content[7][0])) + except: + random_seed_create = None + else: + random_seed_create = None + else: + set_size_ratios = None + n_sets_per_size = None + fix_random_seed_create = None + random_seed_create = None + if train_logical: + n_epochs = abs(int(content[8][0])) + test_fraction = float(content[9][0]) + mpirun_cores = abs(int(content[10][0])) + write_submission_script_logical = convert_yes_no_to_logical(content[11][0]) + if write_submission_script_logical: + try: + maximum_time = str(content[12][0]) + except: + maximum_time = None + else: + maximum_time = None + fix_random_seed_train = convert_yes_no_to_logical(content[13][0]) + if fix_random_seed_train: + try: + random_seed_train = abs(int(content[14][0])) +# print(random_seed_train) + except: + random_seed_train = None + else: + random_seed_train = None + else: + n_epochs = None + test_fraction = None + mpirun_cores = None + write_submission_script_logical = None + maximum_time = None + fix_random_seed_train = None + random_seed_train = None + if External_test_logical: + dataset_cores = abs(int(content[15][0])) + fix_random_seed_predict = convert_yes_no_to_logical(content[16][0]) + if fix_random_seed_predict: + try: + random_seed_predict = abs(int(content[17][0])) + except: + random_seed_predict = None + else: + random_seed_predict = None + else: + dataset_cores = None + fix_random_seed_predict = None + random_seed_predict = None + if Validation_predict_logical: + validation_dataset_cores = abs(int(content[18][0])) + fix_random_seed_validation = convert_yes_no_to_logical(content[19][0]) + if fix_random_seed_validation: + try: + random_seed_validation = abs(int(content[20][0])) + except: + random_seed_validation = None + else: + random_seed_validation = None + else: + validation_dataset_cores = None + fix_random_seed_validation = None + random_seed_validation = None + user_input.close() + return create_logical,train_logical,analyse_logical,plot_logical,\ + External_test_logical,External_analyse_logical,External_plot_logical,\ + Validation_predict_logical,Validation_plot_logical,\ + set_size_ratios,n_sets_per_size,fix_random_seed_create,random_seed_create,\ + n_epochs,test_fraction,mpirun_cores,write_submission_script_logical,maximum_time,fix_random_seed_train,random_seed_train,\ + dataset_cores,fix_random_seed_predict,random_seed_predict,\ + validation_dataset_cores,fix_random_seed_validation,random_seed_validation diff --git a/tools/python/NNTSSD/source/learning_curves.py b/tools/python/NNTSSD/source/learning_curves.py new file mode 100644 index 000000000..92cf9b99e --- /dev/null +++ b/tools/python/NNTSSD/source/learning_curves.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Tools for analysing learning curves and plotting training performance. +""" + +import os +import sys +import shutil +import numpy as np +import matplotlib.pyplot as plt + +class Tools_LC(): + """Tools for analysing and plotting Learning Curves/Training Performance. + + Methods + ------- + analyse_learning_curves() : + Prepares analyse data from the learning curves obtained in training. + plot_training_performance() : + Plots the energy and force RMSE versus number of training epoch. + """ + def __init__(self): + """In the moment, the class Tools_LC() does not have any attributes. + + Still, it makes sense to define a class since it condenses functionalities and + for future purposes, it is likely that attributes will be added. + """ + + def analyse_learning_curves(self): + """Prepares analyse data from the learning curves obtained in training. + + Notes + ----- + REQUIREMENTS: + ``Output/ratio*/ratio*_**`` : 2-layered directory structure + Created with the method NNTSSD.Tools.create_training_datasets(). + ``Output/ratio*/ratio*_**/learning-curve.out`` : file + Contains learning curve data, created with the method NNTSSD.Tools.create_training_datasets(). + ``Output/ratio*/ratio*_**/predict_testdata/learning-curve-testdata.out`` : file + Contains learning curve data of external testset, created with the method NNTSSD.External_Testset.predict_test_data(). + OUTPUTS: + ``Output/training_performance/learning_curve_E.out`` : file + Contains processed mean energy training performance information for all dataset sizes. + ``Output/training_performance/learning_curve_F.out`` : file + Contains processed mean energy training performance information for all dataset sizes. + ``Output/training_performance/learning_curve_Etest_*.out`` : files + Contains processed energy test training performance information for all dataset samples of size *. + ``Output/training_performance/learning_curve_Etrain_*.out`` : files + Contains processed energy train training performance information for all dataset samples of size *. + ``Output/training_performance/learning_curve_Ftest_*.out`` : files + Contains processed force test training performance information for all dataset samples of size *. + ``Output/training_performance/learning_curve_Ftrain_*.out`` : files + Contains processed force train training performance information for all dataset samples of size *. + """ + print("\n***ANALYSING LEARNING CURVES***************************************************") +# print("...",end="\r") + if not os.path.isdir("Output"): + sys.exit("ERROR: The folder 'Output' does not exist!") + else: + os.chdir("Output") + # Finding out number of epochs. + ratio_dir_string = np.sort(os.listdir()) + number_of_epochs = 0 + n_total_configurations = 0 + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + os.chdir(set_dir_string[set_dir_counter]) + try: + learning_curve_data = np.genfromtxt("learning-curve.out") + number_of_epochs = int(learning_curve_data[-1,0]) + nnp_select_log = open("nnp-select.log") + for line in nnp_select_log: + if line.startswith("Total"): + n_total_configurations = int(''.join(filter(str.isdigit, str(line)))) +# os.chdir("../") + except: + continue + os.chdir("../") + if not (number_of_epochs == 0 and n_total_configurations == 0): + break + os.chdir("../") + if not (number_of_epochs == 0 and n_total_configurations == 0): + break + # COLLECT TRAINING PERFORMANCE DATA. + print(" Collecting training performance data.") +# print("...",end="\r") + try: + shutil.rmtree("training_performance") + print("INFO: Removed old 'training_performance' folder.") + except: + pass + os.system("mkdir training_performance") + set_size_ratios = np.array([]) + learning_curve_E = np.zeros((number_of_epochs+1)) + learning_curve_F = np.zeros((number_of_epochs+1)) + ratio_dir_string = np.sort(os.listdir()) + for ratio_dir_counter in range(len(ratio_dir_string)): + if ratio_dir_string[ratio_dir_counter].startswith('ratio'): + current_ratio = 0.01*int(''.join(filter(str.isdigit, ratio_dir_string[ratio_dir_counter]))) + set_size_ratios = np.append(set_size_ratios,current_ratio) + learning_curve_Etrain = np.zeros((number_of_epochs+1)) + learning_curve_Etest = np.zeros((number_of_epochs+1)) + learning_curve_Ftrain = np.zeros((number_of_epochs+1)) + learning_curve_Ftest = np.zeros((number_of_epochs+1)) + os.chdir(ratio_dir_string[ratio_dir_counter]) + set_dir_string = np.sort(os.listdir()) + for set_dir_counter in range(len(set_dir_string)): + if set_dir_string[set_dir_counter].startswith('ratio'): + os.chdir(set_dir_string[set_dir_counter]) + try: + learning_curve_data = np.genfromtxt("learning-curve.out") + learning_curve_Etrain = np.column_stack((learning_curve_Etrain,learning_curve_data[:,1])) + learning_curve_Ftrain = np.column_stack((learning_curve_Ftrain,learning_curve_data[:,3])) + try: + learning_curve_testdata = np.genfromtxt("predict_testdata/learning-curve-testdata.out") + learning_curve_Etest = np.column_stack((learning_curve_Etest,learning_curve_testdata[:,1])) + learning_curve_Ftest = np.column_stack((learning_curve_Ftest,learning_curve_testdata[:,2])) + print("INFO: Calculation with external Testdata.") + except: + print("INFO: Calculation with internal Testdata.") + learning_curve_Etest = np.column_stack((learning_curve_Etest,learning_curve_data[:,2])) + learning_curve_Ftest = np.column_stack((learning_curve_Ftest,learning_curve_data[:,4])) + except: + print("INFO: The file 'learning-curve.out' does not exist.") +# continue + os.chdir("../") + learning_curve_Etrain = learning_curve_Etrain[:,1:] + learning_curve_Etest = learning_curve_Etest[:,1:] + learning_curve_Ftrain = learning_curve_Ftrain[:,1:] + learning_curve_Ftest = learning_curve_Ftest[:,1:] + + np.savetxt("../training_performance/learning_curve_Etrain_"+str("{:3.2f}".format(current_ratio))+".out",learning_curve_Etrain) + np.savetxt("../training_performance/learning_curve_Etest_"+str("{:3.2f}".format(current_ratio))+".out",learning_curve_Etest) + np.savetxt("../training_performance/learning_curve_Ftrain_"+str("{:3.2f}".format(current_ratio))+".out",learning_curve_Ftrain) + np.savetxt("../training_performance/learning_curve_Ftest_"+str("{:3.2f}".format(current_ratio))+".out",learning_curve_Ftest) + + mean_Etrain_data = np.mean(learning_curve_Etrain,axis=1) + mean_Etest_data = np.mean(learning_curve_Etest,axis=1) + std_Etrain_data = np.std(learning_curve_Etrain,axis=1) + std_Etest_data = np.std(learning_curve_Etest,axis=1) + mean_Ftrain_data = np.mean(learning_curve_Ftrain,axis=1) + mean_Ftest_data = np.mean(learning_curve_Ftest,axis=1) + std_Ftrain_data = np.std(learning_curve_Ftrain,axis=1) + std_Ftest_data = np.std(learning_curve_Ftest,axis=1) + ratio_learning_curve_data = np.full_like(mean_Etrain_data,current_ratio) + learning_curve_E = np.column_stack((learning_curve_E,np.column_stack((np.column_stack((np.column_stack((ratio_learning_curve_data,np.column_stack((mean_Etrain_data,std_Etrain_data)))),mean_Etest_data)),std_Etest_data)))) + learning_curve_F = np.column_stack((learning_curve_F,np.column_stack((np.column_stack((np.column_stack((ratio_learning_curve_data,np.column_stack((mean_Ftrain_data,std_Ftrain_data)))),mean_Ftest_data)),std_Ftest_data)))) + + os.chdir("../") + + os.chdir("training_performance") + np.savetxt("learning_curve_E.out",learning_curve_E[:,1:],fmt="%.18e",header='%15s%25s%25s%25s%25s'%('ratio','RMSE_Etrain','std_RMSE_Etrain','mean_RMSE_Etest','std_RMSE_Etest')) + np.savetxt("learning_curve_F.out",learning_curve_F[:,1:],fmt="%.18e",header='%15s%25s%25s%25s%25s'%('ratio','RMSE_Ftrain','std_RMSE_Ftrain','mean_RMSE_Ftest','std_RMSE_Ftest')) + os.chdir("../../") + print("FINISHED analysing learning curves.") + return number_of_epochs, n_total_configurations, set_size_ratios + + def plot_training_performance(self,number_of_epochs, n_total_configurations, set_size_ratios): + """Plots the energy and force RMSE versus number of epochs. + + Firstly, this method plots the mean energy and force RMSE training performance. + Secondly, it plots the energy and force RMSE for all sample sets of each training size. + + Notes + ----- + REQUIREMENTS: + ``Output/training_performance/learning_curve_E.out`` : file + Created with analyse_learning_curves(). + ``Output/training_performance/learning_curve_F.out`` : file + Created with analyse_learning_curves(). + ``Output/training_performance/learning_curve_Etest_*.out`` : files + Created with analyse_learning_curves(). + ``Output/training_performance/learning_curve_Etrain_*.out`` : files + Created with analyse_learning_curves(). + ``Output/training_performance/learning_curve_Ftest_*.out`` : files + Created with analyse_learning_curves(). + ``Output/training_performance/learning_curve_Ftrain_*.out`` : files + Created with analyse_learning_curves(). + OUTPUTS: + ``Output/training_performance/Learning_curve_Energies_mean.png`` : png picture + Shows mean train and test energy RMSE (and its standard deviation) versus epoch number. + ``Output/training_performance/Learning_curve_Forces_mean.png`` : png picture + Shows mean train and test forces RMSE (and its standard deviation) versus epoch number. + ``Output/training_performance/Learning_curve_Energies_all.png`` : png picture + Shows train and test energy RMSE for all sample sets versus epoch number. + ``Output/training_performance/Learning_curve_Forces_all.png`` : png picture + Shows train and test forces RMSE for all sample sets versus epoch number. + """ + print("\n***PLOTTING TRAINING PERFORMANCE***********************************************") +# print("...",end="\r") + if not os.path.isdir("Output/training_performance"): + sys.exit("ERROR: The folder 'Output/training_performance' does not exist!") + else: + os.chdir("Output/training_performance") + if not os.path.isfile("learning_curve_E.out"): + sys.exit("ERROR: The file 'learning_curve_E.out' does not exist!") + if not os.path.isfile("learning_curve_F.out"): + sys.exit("ERROR: The file 'learning_curve_F.out' does not exist!") + + print(" Plotting training performance.") +# print("...",end="\r") + parameters = ["Energies","Forces"] + parameter_shortcuts = ["E","F"] + for parameter_counter in range(len(parameters)): + current_parameter = parameters[parameter_counter] + current_shortcut = parameter_shortcuts[parameter_counter] + learning_curve = np.genfromtxt("learning_curve_"+current_shortcut+".out") + training_set_ratios = learning_curve[0,::5] + epochs = np.arange(len(learning_curve[:,0])) +# plot mean + plt.figure("Learning_curve_"+current_parameter) + for ratios_counter in range(len(training_set_ratios)): + current_training_set_size = training_set_ratios[ratios_counter]*n_total_configurations + plt.errorbar(epochs,learning_curve[:,1+5*ratios_counter],learning_curve[:,2+5*ratios_counter],errorevery=(17+ratios_counter),linewidth=1,capsize=2,ls="-",color=plt.cm.Pastel2(ratios_counter),label=current_shortcut+"train_setsize="+str(int(current_training_set_size)))[-1][0].set_linestyle('--') + plt.errorbar(epochs,learning_curve[:,3+5*ratios_counter],learning_curve[:,4+5*ratios_counter],errorevery=(18+ratios_counter),linewidth=1,capsize=2,ls="-",color=plt.cm.Dark2(ratios_counter), label=current_shortcut+"test_setsize="+str(int(current_training_set_size))) + plt.yscale('log') + plt.title("Training performance for "+current_parameter+" (mean)") + plt.xlabel("epoch") + if current_parameter == "Energies": + plt.ylabel(r"Energy RMSE (meV/atom)") + elif current_parameter == "Forces": + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("Learning_curve_"+current_parameter+"_mean.png",dpi=300) +# plot all + plt.figure("Learning_curve_"+current_shortcut+"_all") + for ratios_counter in range(len(training_set_ratios)): + current_ratio = training_set_ratios[ratios_counter] + learning_curve_train = np.genfromtxt("learning_curve_"+current_shortcut+"train_"+str("{:3.2f}".format(current_ratio))+".out") + learning_curve_test = np.genfromtxt("learning_curve_"+current_shortcut+"test_"+str("{:3.2f}".format(current_ratio))+".out") + os.remove("learning_curve_"+current_shortcut+"train_"+str("{:3.2f}".format(current_ratio))+".out") + os.remove("learning_curve_"+current_shortcut+"test_"+str("{:3.2f}".format(current_ratio))+".out") + for samples_counter in range(len(learning_curve_train[0,:])): + plt.plot(epochs,learning_curve_train[:,samples_counter],linewidth=1,ls="-",color=plt.cm.Pastel2(ratios_counter),label=current_shortcut+"train_setsize="+str(int(current_ratio*n_total_configurations))) + plt.plot(epochs,learning_curve_test[:,samples_counter],linewidth=1,ls="-",color=plt.cm.Dark2(ratios_counter),label=current_shortcut+"test_setsize="+str(int(current_ratio*n_total_configurations))) + plt.yscale('log') + plt.title("Training performance for "+current_parameter) + plt.xlabel("epoch") + if current_parameter == "Energies": + plt.ylabel(r"Energy RMSE (meV/atom)") + elif current_parameter == "Forces": + plt.ylabel(r"Forces RMSE (meV/$\AA$)") + plt.grid(b=True, which='major', color='#999999', linestyle=':') + plt.legend(loc=1, borderaxespad=0.1) + plt.savefig("Learning_curve_"+current_parameter+"_all.png",dpi=300) + + os.chdir("../../") + print("FINISHED plotting training performance.") + return None + + +def perform_Learning_curves(): + """This function performs methods of the class Tools_LC(). + + Firstly, it performs the method Tools_LC.analyse_learning_curves(). + Secondly, it performs the method Tools_LC.plot_training_performance(). + """ + print("*******************************************************************************") + print("Analysing learning curves and plotting training performance") + print("*******************************************************************************") + + myClass = Tools_LC() + + number_of_epochs, n_total_configurations, set_size_ratios = myClass.analyse_learning_curves() + myClass.plot_training_performance(number_of_epochs, n_total_configurations, set_size_ratios) + return None + +def main(): + pass +if __name__=="__main__": + perform_Learning_curves() diff --git a/tools/python/NNTSSD/source/preparation.py b/tools/python/NNTSSD/source/preparation.py new file mode 100644 index 000000000..5273bcb95 --- /dev/null +++ b/tools/python/NNTSSD/source/preparation.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 26 12:38:36 2019 + +@author: mr +""" + +import split_data +import os +import sys +import shutil +import numpy as np + +def prepare_original_data(pattern_foldername,sample_number): + for sample_counter in range(1,sample_number+1): + shutil.copytree(pattern_foldername,"Sample_"+str(sample_counter)+"_"+pattern_foldername) + os.chdir("Sample_"+str(sample_counter)+"_"+pattern_foldername) + try: + shutil.rmtree("split_data") + print("INFO: Removed old 'predict_testdata' folder.") + except: + pass + os.system("mkdir split_data") + split_data.split_data(0.1) + shutil.copy("test.data","predict_test_data/input.data") + shutil.copy("input.data","original.data") + shutil.copy("train.data","input.data") + os.chdir("../") + +def perform(): + prepare_original_data("XXXX",5) + +def main(): + pass +if __name__=="__main__": + perform() \ No newline at end of file diff --git a/tools/python/NNTSSD/source/split_data.py b/tools/python/NNTSSD/source/split_data.py new file mode 100644 index 000000000..c03010c7c --- /dev/null +++ b/tools/python/NNTSSD/source/split_data.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 18 13:10:59 2019 + +@author: mr +""" + +import re +import numpy as np + +def split_data(test_fraction): + """Splits the file input.data in two files wrt given fraction. + + Idea taken from: `https://stackoverflow.com/questions/35916503/how-can-i-split-a-text-file-into-multiple-text-files-using-python`_ + + Parameters + ---------- + test_fraction : float + Desired ratio of dataset that is kept for testing, eg. 0.1 means 10 percent. + + Notes + ----- + Requirements + ``input.data`` : file + Contains original dataset. + Outputs + ``test.data`` : file + Contains specified fraction of the original dataset, randomly choosen. + ``train.data`` : file + Contains remaining data. + + """ + with open('input.data', 'r') as f: + data = f.read() + + found = re.findall(r'\n*( begin.*?\n end)\n*', data, re.M | re.S) + + for i in range(len(found)): + if (np.random.rand() > test_fraction): + open('train.data','a').write(found[i]+'\n') + else: + open('test.data','a').write(found[i]+'\n') + +def perform(): + split_data(0.1) + +def main(): + pass +if __name__=="__main__": + perform() \ No newline at end of file diff --git a/tools/python/NNTSSD/tests/NNTSSD_input.dat b/tools/python/NNTSSD/tests/NNTSSD_input.dat new file mode 100644 index 000000000..72b26d839 --- /dev/null +++ b/tools/python/NNTSSD/tests/NNTSSD_input.dat @@ -0,0 +1,60 @@ +# INPUT SPECIFICATIONS FOR RUNNING NNTSSD: +################################################################################ +# Specify which NNTSSD.Tools steps shall be performed: +################################################################################ +# Create datasets/training/analyse learning curves/plot size dependence? +# (y/n, separated by blank space): +y y y y +# In case of external testdata: predict data/analyse learning curves/plot SSD? +y y y +# Predict validation data/plot size dependence? +y y +################################################################################ +# Specify parameters for NNTSSD.Tools.create_training_datatsets: +################################################################################ +# Give a list of the desired set size ratios, separated by blank space: + +# Alternatively: Give minimum, maximum and step size of desired ratios, +# separated by blank space (used if above is empty): +0.8 0.9 0.1 +# Give the number of sample datasets per training size: +2 +# Do you wish to fix the random generator seed to a specific value? (y/n) +y +# If yes, give the random generator seed (optional, default is 123): +456 +################################################################################ +# Specify parameters for NNTSSD.Tools.training_neural_network: +################################################################################ +# Give the desired number of training epochs: +2 +# Give the fraction of data you want to keep for testing (nnp-internal): +0.1 +# Give the number of cores you want to use for mpirun (nnp-train): +4 +# Write a VSC submission script (if not, training is performed on your machine)? +y +# If yes, give the maximum time required for exectuing job (hh:mm:ss, optional): +08:00:00 +# Do you wish to fix the random generator seed to a specific value? (y/n) +y +# If yes, give the random generator seed (optional, default is 123): +789 +################################################################################ +# Specify parameters for NNTSSD.External_Testdata.predict_test_data: +################################################################################ +# Give the number of cores you want to use for mpirun (nnp-dataset): +4 +# Do you wish to fix the random generator seed to a specific value? (y/n) +y +# If yes, give the random generator seed (optional, default is 123): +234 +################################################################################ +# Specify parameters for NNTSSD.Validation.predict_validation_data: +################################################################################ +# Give the number of cores you want to use for mpirun (nnp-dataset): +4 +# Do you wish to fix the random generator seed to a specific value? (y/n) +y +# If yes, give the random generator seed (optional, default is 123): +567 diff --git a/tools/python/NNTSSD/tests/NNTSSD_input_old.dat b/tools/python/NNTSSD/tests/NNTSSD_input_old.dat new file mode 100644 index 000000000..e101d45a9 --- /dev/null +++ b/tools/python/NNTSSD/tests/NNTSSD_input_old.dat @@ -0,0 +1,36 @@ +# INPUT SPECIFICATIONS FOR RUNNING NNTSSD: +################################################################################ +# Specify which NNTSSD.Tools steps shall be performed: +################################################################################ +# Create datasets/training/analyse learning curves/plot size dependence? +# (y/n, separated by blank space): +y y n n +################################################################################ +# Specify parameters for NNTSSD.Tools.create_training_datatsets: +################################################################################ +# Give a list of the desired set size ratios, separated by blank space: + +# Alternatively: Give minimum, maximum and step size of desired ratios, +# separated by blank space (used if above is empty): +0.8 0.9 0.1 +# Give the number of sample datasets per training size: +2 +# Do you wish to fix the random generator seed to a specific value? (y/n) +y +# If yes, give the random generator seed (optional, default is 123): +456 +################################################################################ +# Specify parameters for NNTSSD.Tools.training_neural_network: +################################################################################ +# Give the desired number of training epochs: +2 +# Give the number of cores you want to use for mpirun: +4 +# Write a VSC submission script (if not, training is performed on your machine)? +y +# If yes, give the maximum time required for exectuing the job (optional): +08:00:00 +# Do you wish to fix the random generator seed to a specific value? 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23.403316849 2.478210504 H 0.123135500 0.000000000 -0.006368839 0.004516237 -0.013166446 +atom 0.169763614 -0.170292738 5.058420906 H 0.104258450 0.000000000 0.006641123 -0.000339229 0.001710832 +atom 22.768355390 15.179884991 22.329029795 O -0.238086150 0.000000000 -0.046086010 -0.025235881 0.005743850 +atom 21.434125066 15.631748923 -0.333185305 H 0.126220700 0.000000000 0.031123799 -0.009001222 -0.035124767 +atom 21.989842718 13.702640137 21.289668674 H 0.119659510 0.000000000 0.013004599 0.035317132 0.026225131 +atom 12.222509671 8.189955346 1.618074860 O -0.247919730 0.000000000 -0.001797123 0.017574523 -0.067071366 +atom 12.278849988 7.760085945 3.281828197 H 0.111233780 0.000000000 -0.004235384 -0.012471568 0.070548691 +atom 13.880380289 7.710926590 1.035681823 H 0.141482170 0.000000000 0.006536703 0.000642119 -0.006764662 +atom 13.661907075 10.667631057 11.112100488 O -0.216001700 0.000000000 -0.037279496 -0.013776615 0.034916697 +atom 14.124858034 12.345145386 11.672595370 H 0.133272180 0.000000000 0.003591734 0.013021971 0.000895419 +atom 12.435880622 10.173618657 12.581225186 H 0.147421220 0.000000000 0.029632111 0.008032176 -0.031477484 +atom 17.486330661 17.032057327 9.295375649 O -0.235087510 0.000000000 -0.006875504 -0.012809861 -0.043019615 +atom 16.911783770 16.107704983 7.688512539 H 0.110665490 0.000000000 0.006713966 0.011433370 0.028267991 +atom 18.328113278 18.516190235 8.528021815 H 0.118898150 0.000000000 -0.000175840 -0.004542786 0.012591957 +atom 4.935770073 18.663509563 6.368200054 O -0.194094220 0.000000000 0.031139053 -0.066276697 0.021098669 +atom 6.518835853 17.598527525 5.812888693 H 0.156894820 0.000000000 -0.042755679 0.033516151 0.011475343 +atom 4.178872098 17.417123193 7.699892475 H 0.137280880 0.000000000 0.015711127 0.037753788 -0.030393813 +atom 21.151675148 20.879473220 7.686178727 O -0.248140030 0.000000000 -0.009994913 -0.003084252 0.015181890 +atom 21.725161902 21.081677775 5.962589077 H 0.096696550 0.000000000 0.004375907 0.001697312 -0.018251199 +atom 22.673568579 21.355735416 8.583993635 H 0.139564520 0.000000000 -0.002909430 -0.001404868 0.006677838 +atom 19.507253717 12.301401988 18.501302967 O -0.199717660 0.000000000 -0.028410932 -0.035128726 0.043667183 +atom 19.776549245 13.758948329 17.594513745 H 0.142371980 0.000000000 0.012693146 0.047711282 -0.031771675 +atom 17.867647309 12.681150163 19.378767407 H 0.141514070 0.000000000 0.014688099 -0.002133776 -0.011109509 +atom 23.134797998 17.370023640 17.326635511 O -0.251377170 0.000000000 -0.000268730 -0.000574628 -0.003573364 +atom 23.309537263 17.766571487 19.122904057 H 0.118237060 0.000000000 0.005638293 -0.001455468 -0.001185792 +atom 0.463480083 18.316502796 16.477809882 H 0.138644360 0.000000000 -0.009964426 -0.001923402 0.004419419 +atom 7.198349513 9.467121587 20.207068712 O -0.243428280 0.000000000 0.009787410 -0.009154114 0.016239349 +atom 7.841261053 8.491977165 21.715395794 H 0.127971510 0.000000000 -0.015877818 0.012471507 -0.019846659 +atom 5.641732350 10.325405299 20.696485297 H 0.130413120 0.000000000 0.003877775 0.000013170 0.004937889 +energy -4900.14395256 +charge 0.0000000000 + end diff --git a/tools/python/NNTSSD/tests/input.nn b/tools/python/NNTSSD/tests/input.nn new file mode 100644 index 000000000..b4a685b8e --- /dev/null +++ b/tools/python/NNTSSD/tests/input.nn @@ -0,0 +1,187 @@ +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.3353824022135491E-01 +conv_energy 8.5167544626563699E+02 +conv_length 2.2798325235535355E+01 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +atom_energy H -0.45890771 # Free atom reference energy (H). +atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type (optional argument: shift parameter alpha). +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# ADDITIONAL SETTINGS FOR TRAINING +############################################################################### +# These keywords are solely used for training with nnp-train. +epochs 4 # Number of training epochs. +updater_type 1 # Weight update method (0 = Gradient Descent, 1 = Kalman filter). +parallel_mode 0 # Training parallelization used (0 = Parallel (rank 0 update), 1 = Parallel (all update)). +jacobian_mode 1 # Jacobian computation mode (0 = Summation to single gradient, 1 = Per-task summed gradient, 2 = Full Jacobian). +update_strategy 0 # Update strategy (0 = Combined, 1 = Per-element). +selection_mode 2 # Update candidate selection mode (0 = Random, 1 = Sort, 2 = Threshold). +task_batch_size_energy 1 # Number of energy update candidates prepared per task for each update (0 = Entire training set). +task_batch_size_force 1 # Number of force update candidates prepared per task for each update (0 = Entire training set). +memorize_symfunc_results # Keep symmetry function results in memory. +test_fraction 0 # Fraction of structures kept for testing. +force_weight 10.0 # Weight of force updates relative to energy updates. +short_energy_fraction 1.000 # Fraction of energy updates per epoch. +short_force_fraction 0.0041 # Fraction of force updates per epoch. +short_energy_error_threshold 0.00 # RMSE threshold for energy update candidates. +short_force_error_threshold 1.00 # RMSE threshold for force update candidates. +rmse_threshold_trials 3 # Maximum number of RMSE threshold trials. +#use_old_weights_short # Restart fitting with old weight parameters. +weights_min -1.0 # Minimum value for initial random weights. +weights_max 1.0 # Maximum value for initial random weights. +#precondition_weights # Precondition weights with initial energies. +#nguyen_widrow_weights_short # Initialize neural network weights according to Nguyen-Widrow scheme. +write_trainpoints 1 # Write energy comparison every this many epochs. +write_trainforces 1 # Write force comparison every this many epochs. +write_weights_epoch 1 # Write weights every this many epochs. +write_neuronstats 1 # Write neuron statistics every this many epochs. +write_trainlog # Write training log file. +#################### +# GRADIENT DESCENT # +#################### +# This section is only used if "updater_type" is "0". +gradient_type 1 # Gradient descent type (0 = Fixed step size, 1 = Adam). +gradient_eta 1.0E-5 # Fixed step size gradient descent parameter eta. +gradient_adam_eta 1.0E-3 # Adam parameter eta. +gradient_adam_beta1 0.9 # Adam parameter beta1. +gradient_adam_beta2 0.999 # Adam parameter beta2. +gradient_adam_epsilon 1.0E-8 # Adam parameter epsilon. +############################ +# KALMAN FILTER (STANDARD) # +############################ +# This section is only used if "updater_type" is "1". +kalman_type 0 # Kalman filter type (0 = Standard, 1 = Fading memory). +kalman_epsilon 1.0E-2 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +kalman_q0 0.01 # General Kalman filter parameter q0 ("large"). +kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +kalman_qmin 1.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +kalman_eta 0.01 # Standard Kalman filter parameter eta (0.001-1.0). +kalman_etatau 2.302 # Standard Kalman filter parameter etatau (2.302 => 1 order of magnitude per epoch). +kalman_etamax 1.0 # Standard Kalman filter parameter etamax (1.0+). +################################# +# KALMAN FILTER (FADING MEMORY) # +################################# +# This section is only used if "updater_type" is "1". +# The settings here enable an alternative Kalman filter variant and are NOT RECOMMENDED! +#kalman_type 1 # Kalman filter type (0 = Standard, 1 = Fading memory). +#kalman_epsilon 1.0E-1 # General Kalman filter parameter epsilon (sigmoidal: 0.01, linear: 0.001). +#kalman_q0 0.00 # General Kalman filter parameter q0 ("large"). +#kalman_qtau 2.302 # General Kalman filter parameter qtau (2.302 => 1 order of magnitude per epoch). +#kalman_qmin 0.0E-6 # General Kalman filter parameter qmin (typ. 1.0E-6). +#kalman_lambda_short 0.96000 # Fading memory Kalman filter parameter lambda (forgetting factor 0.95-0.99). +#kalman_nue_short 0.99950 # Fading memory Kalman filter parameter nu (0.99-0.9995). + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +# NOTE: <> is optional! +#symfunction_short 3 < + +# Wide Angular symmetry function (type 9): +# NOTE: <> is optional! +#symfunction_short 9 < + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/tests/predict_test_data/input.data b/tools/python/NNTSSD/tests/predict_test_data/input.data new file mode 100644 index 000000000..b70742706 --- /dev/null +++ b/tools/python/NNTSSD/tests/predict_test_data/input.data @@ -0,0 +1,2224 @@ + begin +comment written by RuNNerDecom +lattice 24.623508 0.000000 0.000000 +lattice 0.000000 24.623508 0.000000 +lattice 0.000000 0.000000 24.623508 +atom 3.735290838 2.327841157 3.501304856 O -0.226389360 0.000000000 -0.006530137 -0.002852459 -0.027992554 +atom 5.246524320 3.393061301 3.627368536 H 0.120793430 0.000000000 0.004517488 -0.000517262 0.005052573 +atom 3.558676964 2.038939712 1.628158442 H 0.127225380 0.000000000 0.000095815 0.002211264 0.023427576 +atom 4.652261909 21.146799653 16.174455806 O -0.240350190 0.000000000 -0.000231046 -0.007150914 0.026944723 +atom 4.197045601 22.522898765 15.074691455 H 0.132518870 0.000000000 -0.001535223 0.002790532 -0.018391035 +atom 5.012047009 19.588530870 15.274057640 H 0.135733700 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0.000000000 0.029632111 0.008032176 -0.031477484 +atom 17.486330661 17.032057327 9.295375649 O -0.235087510 0.000000000 -0.006875504 -0.012809861 -0.043019615 +atom 16.911783770 16.107704983 7.688512539 H 0.110665490 0.000000000 0.006713966 0.011433370 0.028267991 +atom 18.328113278 18.516190235 8.528021815 H 0.118898150 0.000000000 -0.000175840 -0.004542786 0.012591957 +atom 4.935770073 18.663509563 6.368200054 O -0.194094220 0.000000000 0.031139053 -0.066276697 0.021098669 +atom 6.518835853 17.598527525 5.812888693 H 0.156894820 0.000000000 -0.042755679 0.033516151 0.011475343 +atom 4.178872098 17.417123193 7.699892475 H 0.137280880 0.000000000 0.015711127 0.037753788 -0.030393813 +atom 21.151675148 20.879473220 7.686178727 O -0.248140030 0.000000000 -0.009994913 -0.003084252 0.015181890 +atom 21.725161902 21.081677775 5.962589077 H 0.096696550 0.000000000 0.004375907 0.001697312 -0.018251199 +atom 22.673568579 21.355735416 8.583993635 H 0.139564520 0.000000000 -0.002909430 -0.001404868 0.006677838 +atom 19.507253717 12.301401988 18.501302967 O -0.199717660 0.000000000 -0.028410932 -0.035128726 0.043667183 +atom 19.776549245 13.758948329 17.594513745 H 0.142371980 0.000000000 0.012693146 0.047711282 -0.031771675 +atom 17.867647309 12.681150163 19.378767407 H 0.141514070 0.000000000 0.014688099 -0.002133776 -0.011109509 +atom 23.134797998 17.370023640 17.326635511 O -0.251377170 0.000000000 -0.000268730 -0.000574628 -0.003573364 +atom 23.309537263 17.766571487 19.122904057 H 0.118237060 0.000000000 0.005638293 -0.001455468 -0.001185792 +atom 0.463480083 18.316502796 16.477809882 H 0.138644360 0.000000000 -0.009964426 -0.001923402 0.004419419 +atom 7.198349513 9.467121587 20.207068712 O -0.243428280 0.000000000 0.009787410 -0.009154114 0.016239349 +atom 7.841261053 8.491977165 21.715395794 H 0.127971510 0.000000000 -0.015877818 0.012471507 -0.019846659 +atom 5.641732350 10.325405299 20.696485297 H 0.130413120 0.000000000 0.003877775 0.000013170 0.004937889 +energy -4900.14395256 +charge 0.0000000000 + end diff --git a/tools/python/NNTSSD/tests/predict_test_data/input.nn b/tools/python/NNTSSD/tests/predict_test_data/input.nn new file mode 100644 index 000000000..641276488 --- /dev/null +++ b/tools/python/NNTSSD/tests/predict_test_data/input.nn @@ -0,0 +1,128 @@ +############################################################################### +# HDNNP for water H2O +############################################################################### +# Length unit : Bohr +# Energy unit : Ha +# Reference method: RPBE-D3 +############################################################################### + +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.5521343547039809E+01 +conv_energy 2.4265748255366972E+02 +conv_length 5.8038448995319847E+00 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +#atom_energy H -0.45890771 # Free atom reference energy (H). +#atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type. +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +#symfunction_short 3 + +# Wide Angular symmetry function (type 9): +#symfunction_short 9 + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/tests/predict_test_data/scaling.data b/tools/python/NNTSSD/tests/predict_test_data/scaling.data new file mode 100644 index 000000000..2a9e0ad7a --- /dev/null +++ b/tools/python/NNTSSD/tests/predict_test_data/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 +# e_index sf_index sf_min sf_max sf_mean sf_sigma +######################################################################################################################### + 1 1 1.0882016636170764E+00 9.6166419119419064E+00 2.2691752247542194E+00 6.7883526611658462E-01 + 1 2 7.3274438904180561E-01 5.0028559321574191E+00 1.3272332317543580E+00 3.3936750181780473E-01 + 1 3 7.6010783783215696E-01 7.1427942966219815E+00 1.6470726712825305E+00 5.0771115927383836E-01 + 1 4 5.4842285884800812E-01 3.7661771168267726E+00 1.0163698211361718E+00 2.5362958053787776E-01 + 1 5 4.0080665126604625E-01 4.1469832401668629E+00 9.0925040981537897E-01 2.9758019277508319E-01 + 1 6 3.6209352253798227E-01 2.2678239402766054E+00 6.4931154122889623E-01 1.4835420345383032E-01 + 1 7 1.8919103878435897E-01 2.2292652677252804E+00 4.5693857051003817E-01 1.5976079618578123E-01 + 1 8 2.6704178695764313E-01 1.3208742362468955E+00 4.2395636902644862E-01 8.0492394978461931E-02 + 1 9 2.4513099752055156E-01 9.4751160662053002E-01 3.6244199023263673E-01 5.2993540556109331E-02 + 1 10 2.2248910067848982E-01 2.7596216013647377E+00 5.3891576898130766E-01 2.0137334230483950E-01 + 1 11 1.4743601726548086E-01 5.5599270746969276E-01 2.6773972195910817E-01 2.6188094566404998E-02 + 1 12 9.9110926426029380E-02 1.7265405335201480E+00 2.9553976311554875E-01 1.1619768775752932E-01 + 1 13 6.5093699123904267E-02 3.4521757733971170E-01 1.8521249136783141E-01 1.9741155185936318E-02 + 1 14 3.1653527247865069E-02 9.1293170125596168E-01 1.5025164684953513E-01 5.3480187368038674E-02 + 1 15 2.9202821602466694E-03 2.6453981776124141E-01 7.6525296616004684E-02 1.8780956137549487E-02 + 1 16 3.2145385719803329E-04 2.8696425565429240E-01 4.5792284631233672E-02 2.3263495133568998E-02 + 1 17 2.4693757528509622E-04 1.3848731138266304E-01 1.7693289653297604E-02 9.7460303038080908E-03 + 1 18 5.0992836797990751E-03 5.8319173651547385E-01 2.3851656540978389E-02 3.7790771891778152E-02 + 1 19 3.2282960174310170E-04 2.1613962298381925E-01 1.7072560754702336E-02 1.4026518665786077E-02 + 1 20 4.9647513277769700E-02 1.6851617426880194E+00 1.4541325969622534E-01 1.0954306125703028E-01 + 1 21 3.4073471604482227E-03 3.1637071808861689E-01 1.8422597685566724E-02 2.0125274191649719E-02 + 1 22 1.3121382132811807E-04 1.0258348935693713E-01 6.3684016949344113E-03 6.6071626858835051E-03 + 1 23 3.3813162813665906E-02 9.1618560879938926E-01 8.1266384503339575E-02 5.7918502576695730E-02 + 1 24 4.1708500446352870E-04 1.5785966980407021E-01 4.6646981268568697E-03 9.8630700614506465E-03 + 1 25 7.3528900917695290E-04 5.9225627251013026E-02 3.7042174075139758E-03 3.3118079036492621E-03 + 1 26 8.9828333062972592E-03 1.9426085555380754E-01 2.4093377110646338E-02 1.0980657457661532E-02 + 1 27 2.1228022180417653E-04 8.7777813240869640E-03 2.0550705761547970E-03 5.8802103858137246E-04 + 2 1 1.5142595331454245E+00 1.0005711988559998E+01 2.6544664635087183E+00 6.7806617585688911E-01 + 2 2 4.4366445360926199E-01 4.6195409357987076E+00 9.6587051599896101E-01 3.3688559575009042E-01 + 2 3 1.1907810568758714E+00 7.5323544094345003E+00 2.0327396422723472E+00 5.0607867531004169E-01 + 2 4 2.7576036468694687E-01 3.3862131032504492E+00 6.5929732667024776E-01 2.5004687333979903E-01 + 2 5 8.0580777590695674E-01 4.5356481255168557E+00 1.2986230824577940E+00 2.9449908325462404E-01 + 2 6 1.0517053799863604E-01 1.8909877539194515E+00 3.0673921331641835E-01 1.4198497108573313E-01 + 2 7 5.6949141690859706E-01 2.6154328621607852E+00 8.4791273805289546E-01 1.5714071578589769E-01 + 2 8 2.3251646720171416E-02 9.3641034200657891E-01 1.1140979781150941E-01 6.9796654369842781E-02 + 2 9 5.1354161698115419E-01 1.8545341781448565E+00 7.2488398046527269E-01 9.8011511620611044E-02 + 2 10 1.1057465545812291E-01 2.9121456897811342E+00 4.7474421797982730E-01 2.3441807910092233E-01 + 2 11 3.5269317308496489E-01 1.0714592032613128E+00 5.3547944391821678E-01 4.5179661104166338E-02 + 2 12 3.0424313539726355E-02 2.5277642768509305E+00 3.1652845366685045E-01 2.1026891409654727E-01 + 2 13 1.5980022688828247E-01 6.6348817066386512E-01 3.7042498273566293E-01 3.0753700953611234E-02 + 2 14 2.7781847150922931E-03 2.3030057819082539E+00 1.7737800292869690E-01 1.8600239464755819E-01 + 2 15 9.5641036809349829E-03 3.9085233064570807E-01 1.5305059323200970E-01 2.7862233984302390E-02 + 2 16 3.7500170432292374E-06 2.0367068825281995E+00 5.4144316535640342E-02 1.4305857218443538E-01 + 2 17 2.4726232100491033E-03 3.4335400617385042E-01 1.6684597803376652E-02 2.1902951351570905E-02 + 2 18 1.7405672406959600E-05 5.6319316766205302E-02 9.5478184601751693E-04 3.3588039002222358E-03 + 2 19 5.4785372164647961E-02 3.0182597583971442E+00 2.0392031625072374E-01 2.0088721011517138E-01 + 2 20 1.3795234987637416E-03 4.9878800454061323E-01 1.2788265359933434E-02 3.1829452602194934E-02 + 2 21 6.6852772684814245E-03 2.6739582842775905E-01 3.0851859894574358E-02 1.7089886758420030E-02 + 2 22 1.7021399438214336E-02 1.4167796508898451E+00 7.6274174813506748E-02 9.2852504206357669E-02 + 2 23 1.9759831791959857E-02 4.0756378297923890E-01 4.8843503112397949E-02 2.5474332458885439E-02 + 2 24 5.2768632746659245E-04 2.3324050667069166E-01 7.2057238727819412E-03 1.4495435261027742E-02 + 2 25 1.1144879740881719E-05 3.5285772934088612E-02 4.2545240948261025E-04 2.0471375111485984E-03 + 2 26 1.6013752685265073E-02 8.2245409953473059E-01 5.0845479076508403E-02 5.2802834522172923E-02 + 2 27 3.9898424495541764E-03 7.8557031440100300E-01 3.6926675414383096E-02 5.0474458307624794E-02 + 2 28 4.0523818189746699E-05 9.8448068666705968E-02 1.2119235889230262E-03 5.7945700128174639E-03 + 2 29 6.0374649986214514E-03 9.9251766407842473E-02 1.6156539248049700E-02 5.5245068674135743E-03 + 2 30 2.9595491075765732E-03 1.5478537567691833E-01 1.1641055270110553E-02 8.9415193910804703E-03 diff --git a/tools/python/NNTSSD/tests/scaling.data b/tools/python/NNTSSD/tests/scaling.data new file mode 100644 index 000000000..94e6d9314 --- /dev/null +++ b/tools/python/NNTSSD/tests/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 +# e_index sf_index sf_min sf_max sf_mean sf_sigma +######################################################################################################################### + 1 1 1.3221693617312411E+00 2.6431829268494309E+00 2.1712109289368211E+00 1.8491740949754865E-01 + 1 2 8.8165507590121150E-01 1.5021513119899590E+00 1.2778122580628937E+00 9.2610524821423193E-02 + 1 3 9.5063170525557483E-01 1.9645434630136041E+00 1.5754555245780282E+00 1.4788592306892495E-01 + 1 4 6.8720926352748557E-01 1.1734596705047000E+00 9.8013669175527418E-01 7.3842159315803318E-02 + 1 5 5.3114680346458376E-01 1.1735104787393884E+00 8.6903918776136291E-01 9.7965057245763557E-02 + 1 6 4.5446803146192011E-01 7.7208808607104440E-01 6.2874675223192622E-01 4.9613886357228562E-02 + 1 7 2.7732917100342269E-01 6.5610689623748530E-01 4.3632718893459976E-01 5.9842739811322473E-02 + 1 8 3.0248121122588545E-01 5.0262343273858556E-01 4.1296646104933438E-01 3.3605991719978266E-02 + 1 9 2.7091763975787958E-01 4.3771867263061898E-01 3.5532262189207031E-01 3.0837550827151866E-02 + 1 10 3.0607810460301704E-01 8.2737294952385632E-01 5.1367846789526717E-01 8.2396345907449139E-02 + 1 11 1.6781481618321603E-01 3.3262544522941584E-01 2.6405877879038225E-01 2.0079773509946830E-02 + 1 12 1.5199899860595487E-01 5.1758403256923147E-01 2.8216876544103786E-01 5.3030699298770759E-02 + 1 13 7.9964213161399539E-02 2.7810985736196053E-01 1.8323412046338658E-01 2.0256908897343304E-02 + 1 14 5.0022559628496853E-02 3.1251552588862541E-01 1.4532438879262641E-01 3.5408976290196639E-02 + 1 15 1.0972758958750323E-02 1.9780403448379363E-01 7.5743125451543408E-02 2.0368083851198718E-02 + 1 16 4.0564819222288929E-03 2.0020915524049140E-01 4.5553334901545346E-02 2.6816652873187446E-02 + 1 17 1.6392409428096447E-03 2.9517020055424961E-02 1.6279759210012987E-02 5.1720047323677923E-03 + 1 18 5.9064708278282335E-03 2.9881994921192988E-02 1.8893635227048098E-02 3.4996722469230767E-03 + 1 19 1.8153163715731997E-03 3.2340440598797260E-02 1.5251396366744187E-02 4.9976078942981228E-03 + 1 20 7.7218851587917817E-02 1.9164313445186615E-01 1.3079740560426881E-01 1.8047175980621721E-02 + 1 21 6.4007973804529085E-03 3.4352270791092009E-02 1.6008839167468644E-02 4.4985079286083869E-03 + 1 22 7.1919475628770427E-04 1.1394644806220967E-02 5.5243272719661379E-03 2.1112600852613100E-03 + 1 23 4.6183461439172707E-02 1.1842669503328147E-01 7.3672710889966644E-02 1.1140003788632082E-02 + 1 24 9.1433846516636246E-04 7.2448079200631453E-03 3.4708660085473373E-03 1.0041987311934573E-03 + 1 25 1.1212823920457925E-03 7.4909848911080303E-03 3.3413578741542951E-03 9.5694251844215597E-04 + 1 26 1.4490733031593294E-02 3.4896169686888266E-02 2.2593555563529325E-02 3.2185820632296114E-03 + 1 27 6.2962266934873338E-04 5.1814478231837560E-03 2.0022000098156752E-03 6.1375837903328550E-04 + 2 1 1.7278003594190612E+00 3.0200705624254782E+00 2.5556245161257864E+00 1.8104733161286587E-01 + 2 2 5.2830234917918673E-01 1.1347830825159733E+00 9.1702802710027076E-01 8.9012140951749405E-02 + 2 3 1.3413913725634967E+00 2.3281910061725970E+00 1.9602733835105492E+00 1.4178250890732702E-01 + 2 4 3.3973454632955868E-01 7.9106176901817504E-01 6.2374175167509027E-01 6.7920469352681950E-02 + 2 5 9.1198642957067100E-01 1.5175524019444977E+00 1.2574935044638531E+00 9.0840547884065884E-02 + 2 6 1.4007970492770014E-01 3.9737354984873224E-01 2.8710959644163986E-01 3.9568021301260234E-02 + 2 7 6.6026020925081808E-01 9.9631802932099356E-01 8.2593292209866853E-01 5.6260988440501335E-02 + 2 8 4.2701723604415549E-02 1.5473052305514301E-01 1.0198348350484386E-01 1.9093898225897559E-02 + 2 9 5.9779238984500482E-01 8.7214154955749057E-01 7.1064524378414085E-01 4.5991882003602061E-02 + 2 10 1.9985378144803514E-01 6.5655897218566894E-01 4.4271239561305453E-01 7.3593849758180452E-02 + 2 11 4.1502563461045067E-01 6.5057595299175830E-01 5.2811755758076440E-01 2.9733597901087361E-02 + 2 12 1.0539027020125716E-01 4.7399188573233930E-01 2.8778725245738251E-01 6.7239561345988727E-02 + 2 13 2.4956202107947600E-01 4.9952217805446975E-01 3.6646824092677327E-01 2.9672553474082333E-02 + 2 14 3.1300696765894295E-02 3.1614843646709484E-01 1.5249418366539025E-01 5.4780305337940489E-02 + 2 15 6.3223700189372165E-02 3.0994899667375020E-01 1.5148625090308679E-01 2.9292310574299443E-02 + 2 16 8.5041679154014842E-04 1.3041346013164093E-01 3.7068123760173058E-02 2.4399391242546552E-02 + 2 17 4.4317519796445081E-03 2.5144073105818975E-02 1.3864844737089635E-02 3.2528315804001926E-03 + 2 18 8.1860993427251071E-05 1.3466045085857874E-03 5.5084028256248212E-04 2.3430301127519475E-04 + 2 19 7.9083037432116490E-02 2.5796793485264985E-01 1.7694123779133991E-01 2.7939522253685803E-02 + 2 20 2.1441033279915650E-03 1.4776262369620617E-02 8.7339425459512163E-03 2.1168859996502751E-03 + 2 21 1.3804891348827978E-02 4.9194759330688440E-02 2.8874387893367045E-02 5.8165890736636416E-03 + 2 22 2.6051407335652452E-02 1.0496356106048180E-01 6.4565049831895718E-02 1.2764836900154985E-02 + 2 23 2.9045826947719750E-02 7.2380719406296798E-02 4.5302566117440039E-02 7.3746817680981859E-03 + 2 24 1.3607528794234713E-03 1.0984596811105142E-02 5.4354345893373179E-03 1.6123179093972310E-03 + 2 25 1.6511705563216422E-05 5.2501188368496877E-04 1.8911718281856260E-04 8.6408752300015161E-05 + 2 26 2.0072212589651677E-02 7.6648328811296088E-02 4.4378983939995231E-02 8.7718911463090226E-03 + 2 27 1.0836132444402223E-02 5.4980675069752655E-02 3.0545338532716584E-02 7.5167668082524765E-03 + 2 28 6.2351340984659318E-05 1.4410461325923972E-03 5.3991094439800945E-04 2.2577401100509146E-04 + 2 29 8.4843170594598311E-03 2.6368291824445372E-02 1.5321153812859305E-02 2.3405182276765900E-03 + 2 30 4.2231192236936999E-03 1.8846153205016307E-02 1.0613759624824323E-02 2.2975215777576064E-03 diff --git a/tools/python/NNTSSD/tests/test_NNTSSD.py b/tools/python/NNTSSD/tests/test_NNTSSD.py new file mode 100644 index 000000000..6da30e200 --- /dev/null +++ b/tools/python/NNTSSD/tests/test_NNTSSD.py @@ -0,0 +1,288 @@ +# -*- coding: utf-8 -*- +""" +06.05.2019 +@author: mr + +PYTHON 3 + +Tests for NNTSSD: Neural Network Training Set Size Dependence +""" +import numpy as np +import sys +sys.path.append("../source") + +import NNTSSD +import os +#%% Tests for NNTSSD.Tools class +def test_create_training_datasets(): + """This function tests the method NNTSSD.Tools.create_training_datasets(). + + It fails if the expected directory structure is not created or there is no file 'input.data' or 'nnp-select.log' in the created directories. + """ + fails = [] + my_Class = NNTSSD.Tools() + my_Class.create_training_datasets(np.array([0.8,0.9]),2,True) + + if not os.path.isdir("Output"): + fails.append("The file 'Output' has not been created.") + if not np.in1d(np.array(['ratio0.80', 'ratio0.90']),np.sort(os.listdir("Output"))).all(): + fails.append("The file structure in 'Output' is not correct.") + if not np.in1d(np.array(['ratio0.80_set1', 'ratio0.80_set2']),np.sort(os.listdir("Output/ratio0.80"))).all(): + fails.append("The file structure in 'Output/ratio0.80' is not correct.") + if not np.in1d(np.array(['ratio0.90_set1', 'ratio0.90_set2']),np.sort(os.listdir("Output/ratio0.90"))).all(): + fails.append("The file structure in 'Output/ratio0.90' is not correct.") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set1/input.data"): + fails.append("The file 'Output/ratio0.80/ratio0.80_set1/input.data' does not exist.") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set2/input.data"): + fails.append("The file 'Output/ratio0.80/ratio0.80_set2/input.data' does not exist.") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set1/input.data"): + fails.append("The file 'Output/ratio0.90/ratio0.90_set1/input.data' does not exist.") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set2/input.data"): + fails.append("The file 'Output/ratio0.90/ratio0.90_set2/input.data' does not exist.") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set1/nnp-select.log"): + fails.append("The file 'Output/ratio0.80/ratio0.80_set1/nnp-select.log' does not exist.") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set2/nnp-select.log"): + fails.append("The file 'Output/ratio0.80/ratio0.80_set2/nnp-select.log' does not exist.") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set1/nnp-select.log"): + fails.append("The file 'Output/ratio0.90/ratio0.90_set1/nnp-select.log' does not exist.") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set2/nnp-select.log"): + fails.append("The file 'Output/ratio0.90/ratio0.90_set2/nnp-select.log' does not exist.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + +def test_training_neural_network(): + """This function tests the method NNTSSD.Tools.training_neural_network(). + + It fails if there is no file 'learning-curve.out' created in the given directories. + """ + fails = [] + my_Class = NNTSSD.Tools() + my_Class.training_neural_network("mpirun -np 4 ../../../../../../../bin/nnp-train",2,0.1,False,True) + + if not os.path.isfile("Output/ratio0.80/ratio0.80_set1/learning-curve.out"): + fails.append("The file 'Output/ratio0.80/ratio0.80_set1/learning-curve.out' does not exist.") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set2/learning-curve.out"): + fails.append("The file 'Output/ratio0.80/ratio0.80_set2/learning-curve.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set1/learning-curve.out"): + fails.append("The file 'Output/ratio0.90/ratio0.90_set1/learning-curve.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set2/learning-curve.out"): + fails.append("The file 'Output/ratio0.90/ratio0.90_set2/learning-curve.out' does not exist.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + +def test_analyse_learning_curves(): + """This function tests the method NNTSSD.Tools.analyse_learning_curves(). + + It fails if the files 'collect_data_***.out' or 'analyse_data_***.out' are not created in the given directories. + """ + fails = [] + my_Class = NNTSSD.Tools() + my_Class.analyse_learning_curves() + + if not os.path.isfile("Output/ratio0.80/collect_data_min_energy.out"): + fails.append("The file 'Output/ratio0.80/collect_data_min_energy.out' does not exist.") + if not os.path.isfile("Output/ratio0.80/collect_data_min_force.out"): + fails.append("The file 'Output/ratio0.80/collect_data_min_force.out' does not exist.") + if not os.path.isfile("Output/ratio0.80/collect_data_min_comb.out"): + fails.append("The file 'Output/ratio0.80/collect_data_min_comb.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/collect_data_min_energy.out"): + fails.append("The file 'Output/ratio0.90/collect_data_min_energy.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/collect_data_min_force.out"): + fails.append("The file 'Output/ratio0.90/collect_data_min_force.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/collect_data_min_comb.out"): + fails.append("The file 'Output/ratio0.90/collect_data_min_comb.out' does not exist.") + if not os.path.isfile("Output/analyse_data_min_energy.out"): + fails.append("The file 'analyse_data_min_energy.out' has not been created.") + if not os.path.isfile("Output/analyse_data_min_force.out"): + fails.append("The file 'analyse_data_min_force.out' has not been created.") + if not os.path.isfile("Output/analyse_data_min_comb.out"): + fails.append("The file 'analyse_data_min_comb.out' has not been created.") + if not os.path.isfile("Output/analyse_data_min_energy_all.out"): + fails.append("The file 'analyse_data_min_energy_all.out' has not been created.") + if not os.path.isfile("Output/analyse_data_min_force_all.out"): + fails.append("The file 'analyse_data_min_force_all.out' has not been created.") + if not os.path.isfile("Output/analyse_data_min_comb_all.out"): + fails.append("The file 'analyse_data_min_comb_all.out' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +def test_plot_size_dependence(): + """This function tests the method NNTSSD.Tools.plot_size_dependence(). + + It fails if there is no file 'Energy_RMSE_epoch_comparison.png' or 'Forces_RMSE_epoch_comparison.png' created in the given directories. + """ + fails = [] + my_Class = NNTSSD.Tools() + my_Class.plot_size_dependence() + + if not os.path.isfile("Output/int_Energy_RMSE_epoch_comparison.png"): + fails.append("The file 'Output/Energy_RMSE_epoch_comparison.png' has not been created.") + if not os.path.isfile("Output/int_Forces_RMSE_epoch_comparison.png"): + fails.append("The file 'Output/Forces_RMSE_epoch_comparison.png' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +#%% Tests for NNTSSD.Validation class +def test_predict_validation_data(): + """This function tests the method NNTSSD.Validation.predict_validation_data(). + + It fails if the directory 'validation' or the files 'validation_data_***.out' are not created in the given directories. + """ + fails = [] + myClass = NNTSSD.Validation() + myClass.predict_validation_data(4,True) + + if not os.path.isdir("Output/ratio0.80/ratio0.80_set1/validation"): + fails.append("The folder 'validation' does not exist in 'Output/ratio0.80/ratio0.80_set1'") + if not os.path.isdir("Output/ratio0.80/ratio0.80_set2/validation"): + fails.append("The folder 'validation' does not exist in 'Output/ratio0.80/ratio0.80_set2'") + if not os.path.isdir("Output/ratio0.90/ratio0.90_set1/validation"): + fails.append("The folder 'validation' does not exist in 'Output/ratio0.90/ratio0.90_set1'") + if not os.path.isdir("Output/ratio0.90/ratio0.90_set2/validation"): + fails.append("The folder 'validation' does not exist in 'Output/ratio0.90/ratio0.90_set2'") + if not os.path.isfile("Output/validation_data_min_comb.out"): + fails.append("The file 'Output/validation_data_min_comb.out' has not been created.") + if not os.path.isfile("Output/validation_data_min_energy.out"): + fails.append("The file 'Output/validation_data_min_energy.out' has not been created.") + if not os.path.isfile("Output/validation_data_min_force.out"): + fails.append("The file 'Output/validation_data_min_force.out' has not been created.") + if not os.path.isfile("Output/validation_data_min_comb_all.out"): + fails.append("The file 'Output/validation_data_min_comb_all.out' has not been created.") + if not os.path.isfile("Output/validation_data_min_energy_all.out"): + fails.append("The file 'Output/validation_data_min_energy_all.out' has not been created.") + if not os.path.isfile("Output/validation_data_min_force_all.out"): + fails.append("The file 'Output/validation_data_min_force_all.out' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +def test_plot_validation_data(): + """This function tests the method NNTSSD.Validation.plot_validation_data(). + + It fails if the files 'val_Energy_***.png' or 'val_Forces_***.png' are not created in the 'Output'-folder. + """ + fails = [] + myClass = NNTSSD.Validation() + myClass.plot_validation_data() + + if not os.path.isfile("Output/val_Energy_RMSE.png"): + fails.append("The file 'Output/val_Energy_RMSE.png' has not been created.") + if not os.path.isfile("Output/val_Forces_RMSE.png"): + fails.append("The file 'Output/val_Forces_RMSE.png' has not been created.") + if not os.path.isfile("Output/val_Energy_RMSE_best_E.png"): + fails.append("The file 'Output/val_Energy_RMSE_best_E.png' has not been created.") + if not os.path.isfile("Output/val_Forces_RMSE_best_F.png"): + fails.append("The file 'Output/val_Forces_RMSE_best_F.png' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +#%% Tests for NNTSSD.External_Testdata class + +def test_predict_test_data(): + """This function tests the method NNTSSD.External_Testdata.predict_test_data(). + + It fails if there is no directory 'predict_testdata' created in the given directory or if no files 'learning-curve-testdata.out' are created inside this directory. + """ + fails = [] + myClass = NNTSSD.External_Testdata() + myClass.predict_test_data(4,True) + + if not os.path.isdir("Output/ratio0.80/ratio0.80_set1/predict_testdata"): + fails.append("The folder 'predict_testdata' does not exist in 'Output/ratio0.80/ratio0.80_set1'") + if not os.path.isdir("Output/ratio0.80/ratio0.80_set2/predict_testdata"): + fails.append("The folder 'predict_testdata' does not exist in 'Output/ratio0.80/ratio0.80_set2'") + if not os.path.isdir("Output/ratio0.90/ratio0.90_set1/predict_testdata"): + fails.append("The folder 'predict_testdata' does not exist in 'Output/ratio0.90/ratio0.90_set1'") + if not os.path.isdir("Output/ratio0.90/ratio0.90_set2/predict_testdata"): + fails.append("The folder 'predict_testdata' does not exist in 'Output/ratio0.90/ratio0.90_set2'") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set1/predict_testdata/learning-curve-testdata.out"): + fails.append("The file 'learning-curve-testdata.out' does not exist in 'Output/ratio0.80/ratio0.80_set1/predict_testdata'") + if not os.path.isfile("Output/ratio0.80/ratio0.80_set2/predict_testdata/learning-curve-testdata.out"): + fails.append("The file 'learning-curve-testdata.out' does not exist in 'Output/ratio0.80/ratio0.80_set2/predict_testdata'") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set1/predict_testdata/learning-curve-testdata.out"): + fails.append("The file 'learning-curve-testdata.out' does not exist in 'Output/ratio0.90/ratio0.90_set1/predict_testdata'") + if not os.path.isfile("Output/ratio0.90/ratio0.90_set2/predict_testdata/learning-curve-testdata.out"): + fails.append("The file 'learning-curve-testdata.out' does not exist in 'Output/ratio0.90/ratio0.90_set2/predict_testdata'") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +def test_analyse_lc_testdata(): + """This function tests the method NNTSSD.External_Testdata.analyse_learning_curves(). + + It fails if the files 'ext_collect_data_***.out' or 'ext_analyse_data_***.out' are not created in the given directories. + """ + fails = [] + myClass = NNTSSD.External_Testdata() + myClass.analyse_learning_curves() + + if not os.path.isfile("Output/ratio0.80/ext_collect_data_min_energy.out"): + fails.append("The file 'Output/ratio0.80/ext_collect_data_min_energy.out' does not exist.") + if not os.path.isfile("Output/ratio0.80/ext_collect_data_min_force.out"): + fails.append("The file 'Output/ratio0.80/ext_collect_data_min_force.out' does not exist.") + if not os.path.isfile("Output/ratio0.80/ext_collect_data_min_comb.out"): + fails.append("The file 'Output/ratio0.80/ext_collect_data_min_comb.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/ext_collect_data_min_energy.out"): + fails.append("The file 'Output/ratio0.90/ext_collect_data_min_energy.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/ext_collect_data_min_force.out"): + fails.append("The file 'Output/ratio0.90/ext_collect_data_min_force.out' does not exist.") + if not os.path.isfile("Output/ratio0.90/ext_collect_data_min_comb.out"): + fails.append("The file 'Output/ratio0.90/ext_collect_data_min_comb.out' does not exist.") + if not os.path.isfile("Output/ext_analyse_data_min_energy.out"): + fails.append("The file 'ext_analyse_data_min_energy.out' has not been created.") + if not os.path.isfile("Output/ext_analyse_data_min_force.out"): + fails.append("The file 'ext_analyse_data_min_force.out' has not been created.") + if not os.path.isfile("Output/ext_analyse_data_min_comb.out"): + fails.append("The file 'ext_analyse_data_min_comb.out' has not been created.") + if not os.path.isfile("Output/ext_analyse_data_min_energy_all.out"): + fails.append("The file 'ext_analyse_data_min_energy_all.out' has not been created.") + if not os.path.isfile("Output/ext_analyse_data_min_force_all.out"): + fails.append("The file 'ext_analyse_data_min_force_all.out' has not been created.") + if not os.path.isfile("Output/ext_analyse_data_min_comb_all.out"): + fails.append("The file 'ext_analyse_data_min_comb_all.out' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +def test_plot_test_size_dependence(): + """This function tests the method NNTSSD.External_Testdata.analyse_learning_curves(). + + It fails if the files 'ext_Energy_RMSE_***.png' or 'ext_Forces_RMSE_***.png' are not created in the 'Output' folder. + """ + fails = [] + myClass = NNTSSD.External_Testdata() + myClass.plot_test_size_dependence() + + if not os.path.isfile("Output/ext_Energy_RMSE.png"): + fails.append("The file 'Output/ext_Energy_RMSE.png' has not been created.") + if not os.path.isfile("Output/ext_Forces_RMSE.png"): + fails.append("The file 'Output/ext_Forces_RMSE.png' has not been created.") + if not os.path.isfile("Output/ext_Energy_RMSE_epoch_compare.png"): + fails.append("The file 'Output/ext_Energy_RMSE_epoch_compare.png' has not been created.") + if not os.path.isfile("Output/ext_Forces_RMSE_epoch_compare.png"): + fails.append("The file 'Output/ext_Forces_RMSE_epoch_compare.png' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + + +#%% Tests for other functions in NNTSSD.py +def test_write_submission_script(): + """This function tests the function write_submission_script. + + It fails if the file 'submit.slrm' is not created with the correct input. + """ + fails = [] + NNTSSD.write_submission_script(str("This is a test command")) + + if not os.path.isfile("submit.slrm"): + fails.append("The file 'submit.slrm' has not been created.") + submission_script = open("submit.slrm","r") + found = False + for line in submission_script: + if str("This is a test command") in line: + found = True + if not found: + fails.append("The command was written incorrectly to the submission script.") \ No newline at end of file diff --git a/tools/python/NNTSSD/tests/test_file_input.py b/tools/python/NNTSSD/tests/test_file_input.py new file mode 100644 index 000000000..32b7625b4 --- /dev/null +++ b/tools/python/NNTSSD/tests/test_file_input.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +06.05.2019 +@author: mr + +PYTHON 3 + +Tests for reading the NNTSSD parameters from file 'NNTSSD_input.dat'. +""" + +import numpy as np +import sys +sys.path.append("../source") + +import file_input + +def test_read_parameters_from_file(): + """This function tests the function read_parameters_from_file(). + + It fails if input file 'NNTSSD_input.dat' is read incorrectly. + """ + fails = [] + + create_logical,train_logical,analyse_logical,plot_logical,\ + External_test_logical,External_analyse_logical,External_plot_logical,\ + Validation_predict_logical,Validation_plot_logical,\ + set_size_ratios,n_sets_per_size,fix_random_seed_create,random_seed_create,\ + n_epochs,test_fraction,mpirun_cores,write_submission_script_logical,maximum_time,fix_random_seed_train,random_seed_train,\ + dataset_cores,fix_random_seed_predict,random_seed_predict,\ + validation_dataset_cores,fix_random_seed_validation,random_seed_validation\ + = file_input.read_parameters_from_file() + + if not create_logical and not train_logical and analyse_logical and plot_logical: + fails.append("The specification on which NNTSSD.Tools steps failed.") + if not External_test_logical and not External_analyse_logical and not External_plot_logical: + fails.append("The specification on which NNTSSD.External_Testdata steps failed.") + if not Validation_predict_logical and not Validation_plot_logical: + fails.append("The specification on which NNTSSD.Validation steps failed.") + if not np.array_equal(set_size_ratios,np.array([0.8,0.9])): + fails.append("The set_size_ratios were read incorrectly from the input file.") + if not n_sets_per_size == 2: + fails.append("The number of samples per set size was read incorrectly from the input file.") + if not fix_random_seed_create == True: + fails.append("The random seed logical for creating datasets is incorrect.") + if not random_seed_create == 456: + fails.append("The random seed for creating datasets is incorrect.") + if not n_epochs == 2: + fails.append("The number of epochs is incorrect.") + if not test_fraction == 0.1: + fails.append("Fraction of data kept for testing is incorrect.") + if not mpirun_cores == 4: + fails.append("Value for mpirun_cores read incorrectly.") + if not write_submission_script_logical == True: + fails.append("The logical for writing a VSC submission script is incorrect.") + if not maximum_time=="08:00:00": + fails.append("The maximum time required for exectuing the job is incorrect.") + if not fix_random_seed_train == True: + fails.append("The random seed logical for training datasets is incorrect.") + if not random_seed_train == 789: + fails.append("The random seed for training datasets is incorrect.") + if not dataset_cores == 4: + fails.append("Value for mpirun_cores (nnp-dataset for external testdata) read incorrectly.") + if not fix_random_seed_predict == True: + fails.append("The random seed logical for predicting external testdata is incorrect.") + if not random_seed_predict == 234: + fails.append("The random seed for predicting external testdata is incorrect.") + if not validation_dataset_cores == 4: + fails.append("Value for mpirun_cores (nnp-dataset for validation) read incorrectly.") + if not fix_random_seed_validation == True: + fails.append("The random seed logical for predicting validation data is incorrect.") + if not random_seed_validation == 567: + fails.append("The random seed for predicting validation data is incorrect.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) \ No newline at end of file diff --git a/tools/python/NNTSSD/tests/test_learning_curves.py b/tools/python/NNTSSD/tests/test_learning_curves.py new file mode 100644 index 000000000..0c3623f3f --- /dev/null +++ b/tools/python/NNTSSD/tests/test_learning_curves.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +06.05.2019 +@author: mr + +PYTHON 3 + +Tests for analysing learning curves and plotting training performance. +""" + +import numpy as np +import sys +sys.path.append("../source") + +import learning_curves +import os + +def test_analyse_learning_curves(): + """This function tests the method analyse_learning_curves(). + + It fails if there is no file 'collect_data.out' or 'analyse_data.out' created in the given directories. + """ + fails = [] + my_Class = learning_curves.Tools_LC() + my_Class.analyse_learning_curves() + + if not os.path.isfile("Output/training_performance/learning_curve_E.out"): + fails.append("The file 'learning_curve_E.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_F.out"): + fails.append("The file 'learning_curve_F.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Etrain_0.80.out"): + fails.append("The file 'learning_curve_Etrain_0.80.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Etest_0.80.out"): + fails.append("The file 'learning_curve_Etest_0.80.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Ftrain_0.80.out"): + fails.append("The file 'learning_curve_Ftrain_0.80.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Ftest_0.80.out"): + fails.append("The file 'learning_curve_Ftest_0.80.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Etrain_0.90.out"): + fails.append("The file 'learning_curve_Etrain_0.90.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Etest_0.90.out"): + fails.append("The file 'learning_curve_Etest_0.90.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Ftrain_0.90.out"): + fails.append("The file 'learning_curve_Ftrain_0.90.out' has not been created.") + if not os.path.isfile("Output/training_performance/learning_curve_Ftest_0.90.out"): + fails.append("The file 'learning_curve_Ftest_0.90.out' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) + +def test_plot_training_performance(): + """This function tests the method plot_training_performance(). + + It fails if the expected *.png files are not created in the given directories. + """ + fails = [] + my_Class = learning_curves.Tools_LC() + my_Class.plot_training_performance(2,20,np.array([0.8,0.9])) + + if not os.path.isfile("Output/training_performance/Learning_curve_Energies_mean.png"): + fails.append("The file 'Output/training_performance/Learning_curve_Energies_mean.png' has not been created.") + if not os.path.isfile("Output/training_performance/Learning_curve_Forces_mean.png"): + fails.append("The file 'Output/training_performance/Learning_curve_Forces_mean.png' has not been created.") + if not os.path.isfile("Output/training_performance/Learning_curve_Energies_all.png"): + fails.append("The file 'Output/training_performance/Learning_curve_Energies_all.png' has not been created.") + if not os.path.isfile("Output/training_performance/Learning_curve_Forces_all.png"): + fails.append("The file 'Output/training_performance/Learning_curve_Forces_all.png' has not been created.") + + assert not fails, "Fails occured:\n{}".format("\n".join(fails)) \ No newline at end of file diff --git a/tools/python/NNTSSD/tests/tests_README b/tools/python/NNTSSD/tests/tests_README new file mode 100644 index 000000000..951379029 --- /dev/null +++ b/tools/python/NNTSSD/tests/tests_README @@ -0,0 +1,2 @@ +in ~/n2p2/tools/python/NNTSSD/tests/, run the following command: +$ python3 -m pytest diff --git a/tools/python/NNTSSD/tests/validation_data/input.data b/tools/python/NNTSSD/tests/validation_data/input.data new file mode 100644 index 000000000..b70742706 --- /dev/null +++ b/tools/python/NNTSSD/tests/validation_data/input.data @@ -0,0 +1,2224 @@ + begin +comment written by RuNNerDecom +lattice 24.623508 0.000000 0.000000 +lattice 0.000000 24.623508 0.000000 +lattice 0.000000 0.000000 24.623508 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+############################################################################### +# Length unit : Bohr +# Energy unit : Ha +# Reference method: RPBE-D3 +############################################################################### + +############################################################################### +# DATA SET NORMALIZATION +############################################################################### +# This section was automatically added by nnp-norm. +mean_energy -2.5521343547039809E+01 +conv_energy 2.4265748255366972E+02 +conv_length 5.8038448995319847E+00 +############################################################################### + +############################################################################### +# GENERAL NNP SETTINGS +############################################################################### +# These keywords are (almost) always required. +number_of_elements 2 # Number of elements. +elements H O # Specification of elements. +#atom_energy H -0.45890771 # Free atom reference energy (H). +#atom_energy O -74.94518524 # Free atom reference energy (O). +cutoff_type 2 # Cutoff type. +scale_symmetry_functions # Scale all symmetry functions with min/max values. +#scale_symmetry_functions_sigma # Scale all symmetry functions with sigma. +scale_min_short 0.0 # Minimum value for scaling. +scale_max_short 1.0 # Maximum value for scaling. +center_symmetry_functions # Center all symmetry functions, i.e. subtract mean value. +global_hidden_layers_short 2 # Number of hidden layers. +global_nodes_short 25 25 # Number of nodes in each hidden layer. +global_activation_short t t l # Activation function for each hidden layer and output layer. +#normalize_nodes # Normalize input of nodes. + +############################################################################### +# ADDITIONAL SETTINGS FOR DATASET TOOLS +############################################################################### +# These keywords are used only by some tools handling data sets: +# nnp-comp2, nnp-scaling, nnp-dataset, nnp-train. +use_short_forces # Use forces. +random_seed 1234567 # Random number generator seed. + +############################################################################### +# SYMMETRY FUNCTIONS +############################################################################### + +# Radial symmetry function (type 2): +#symfunction_short 2 + +# Narrow Angular symmetry function (type 3): +#symfunction_short 3 + +# Wide Angular symmetry function (type 9): +#symfunction_short 9 + +# radial H H +symfunction_short H 2 H 0.001 0.0 12.00 +symfunction_short H 2 H 0.01 0.0 12.00 +symfunction_short H 2 H 0.03 0.0 12.00 +symfunction_short H 2 H 0.06 0.0 12.00 +symfunction_short H 2 H 0.15 1.9 12.00 +symfunction_short H 2 H 0.30 1.9 12.00 +symfunction_short H 2 H 0.60 1.9 12.00 +symfunction_short H 2 H 1.50 1.9 12.00 + +# radial H O / O H +symfunction_short H 2 O 0.001 0.0 12.00 +symfunction_short H 2 O 0.01 0.0 12.00 +symfunction_short H 2 O 0.03 0.0 12.00 +symfunction_short H 2 O 0.06 0.0 12.00 +symfunction_short H 2 O 0.15 0.9 12.00 +symfunction_short H 2 O 0.30 0.9 12.00 +symfunction_short H 2 O 0.60 0.9 12.00 +symfunction_short H 2 O 1.50 0.9 12.00 + +symfunction_short O 2 H 0.001 0.0 12.00 +symfunction_short O 2 H 0.01 0.0 12.00 +symfunction_short O 2 H 0.03 0.0 12.00 +symfunction_short O 2 H 0.06 0.0 12.00 +symfunction_short O 2 H 0.15 0.9 12.00 +symfunction_short O 2 H 0.30 0.9 12.00 +symfunction_short O 2 H 0.60 0.9 12.00 +symfunction_short O 2 H 1.50 0.9 12.00 + +# radial O O +symfunction_short O 2 O 0.001 0.0 12.00 +symfunction_short O 2 O 0.01 0.0 12.00 +symfunction_short O 2 O 0.03 0.0 12.00 +symfunction_short O 2 O 0.06 0.0 12.00 +symfunction_short O 2 O 0.15 4.0 12.00 +symfunction_short O 2 O 0.30 4.0 12.00 +symfunction_short O 2 O 0.60 4.0 12.00 +symfunction_short O 2 O 1.50 4.0 12.00 + +# angular +symfunction_short H 3 O H 0.2 1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.07 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.07 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.07 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 H H 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O H 0.03 -1.0 1.0 12.00000 + +symfunction_short O 3 H H 0.01 1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 1.0 4.0 12.00000 +symfunction_short O 3 H H 0.01 -1.0 4.0 12.00000 +symfunction_short H 3 O H 0.01 -1.0 4.0 12.00000 + +symfunction_short O 3 O H 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O H 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O H 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O H 0.001 -1.0 4.0 12.00000 + +symfunction_short H 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short H 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short H 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short H 3 O O 0.001 -1.0 4.0 12.00000 + +symfunction_short O 3 O O 0.03 1.0 1.0 12.00000 +symfunction_short O 3 O O 0.03 -1.0 1.0 12.00000 +symfunction_short O 3 O O 0.001 1.0 4.0 12.00000 +symfunction_short O 3 O O 0.001 -1.0 4.0 12.00000 diff --git a/tools/python/NNTSSD/tests/validation_data/scaling.data b/tools/python/NNTSSD/tests/validation_data/scaling.data new file mode 100644 index 000000000..2a9e0ad7a --- /dev/null +++ b/tools/python/NNTSSD/tests/validation_data/scaling.data @@ -0,0 +1,72 @@ +################################################################################ +# Symmetry function scaling data. +################################################################################ +# Col Name Description +################################################################################ +# 1 e_index Element index. +# 2 sf_index Symmetry function index. +# 3 sf_min Symmetry function minimum. +# 4 sf_max Symmetry function maximum. +# 5 sf_mean Symmetry function mean. +# 6 sf_sigma Symmetry function sigma. +######################################################################################################################### +# 1 2 3 4 5 6 +# e_index sf_index sf_min sf_max sf_mean sf_sigma +######################################################################################################################### + 1 1 1.0882016636170764E+00 9.6166419119419064E+00 2.2691752247542194E+00 6.7883526611658462E-01 + 1 2 7.3274438904180561E-01 5.0028559321574191E+00 1.3272332317543580E+00 3.3936750181780473E-01 + 1 3 7.6010783783215696E-01 7.1427942966219815E+00 1.6470726712825305E+00 5.0771115927383836E-01 + 1 4 5.4842285884800812E-01 3.7661771168267726E+00 1.0163698211361718E+00 2.5362958053787776E-01 + 1 5 4.0080665126604625E-01 4.1469832401668629E+00 9.0925040981537897E-01 2.9758019277508319E-01 + 1 6 3.6209352253798227E-01 2.2678239402766054E+00 6.4931154122889623E-01 1.4835420345383032E-01 + 1 7 1.8919103878435897E-01 2.2292652677252804E+00 4.5693857051003817E-01 1.5976079618578123E-01 + 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