WTMetaD example

.pre-commit-config.yaml

@@ -8,6 +8,7 @@ repos:
       - id: no-commit-to-branch
       - id: check-executables-have-shebangs
       - id: check-shebang-scripts-are-executable
+        exclude: examples/WTMetaD_paper/r2/free_energy/wtmetad_ib/accumulated_bias/bias_after_iter_15.dat
       - id: check-added-large-files
         args: ['--maxkb=500', '--enforce-all']
         exclude: tests/data/data.zip

examples/WTMetaD_paper/r1/al_downhill/r1_ts.xyz

@@ -0,0 +1,8 @@
+6
+Coordinates from ORCA-job sn2_nebts_NEB-CI_converged
+  C   0.02394151244587     -0.02273675265530      0.05167818806821
+  F   -0.65299103371021      0.72656624059729     -1.73488992550113
+  Cl  0.68562794924814     -0.78844305036752      1.89308430540763
+  H   0.99880860408280      0.16036474652930     -0.38536247269303
+  H   -0.52740777250600      0.82166542425637      0.44671920342826
+  H   -0.52797925956060     -0.89741660836014     -0.27122929870995

examples/WTMetaD_paper/r1/al_downhill/training.py

@@ -0,0 +1,31 @@
+import mlptrain as mlt
+
+mlt.Config.n_cores = 30
+mlt.Config.orca_keywords = ['PBE0', 'def2-SVP', 'EnGrad', 'CPCM(Water)']
+
+if __name__ == '__main__':
+    # Initialise the system
+    system = mlt.System(mlt.Molecule('r1_ts.xyz', charge=-1, mult=1), box=None)
+
+    # Define CV and attach an upper wall
+    avg_r = mlt.PlumedAverageCV(name='avg_r', atom_groups=((0, 1), (0, 2)))
+    avg_r.attach_upper_wall(location=2.5, kappa=1000)
+
+    # Define CVs for extra information
+    r_f = mlt.PlumedAverageCV(name='r_f', atom_groups=(0, 1))
+    r_cl = mlt.PlumedAverageCV(name='r_cl', atom_groups=(0, 2))
+
+    # Initialise PlumedBias
+    bias = mlt.PlumedBias(cvs=(avg_r, r_f, r_cl))
+
+    # Define the potential and train using Downhill AL (fix_init_config=True)
+    ace = mlt.potentials.ACE('r1_downhill', system=system)
+    ace.al_train(
+        method_name='orca',
+        temp=500,
+        n_configs_iter=5,
+        max_active_iters=50,
+        min_active_iters=20,
+        fix_init_config=True,
+        bias=bias,
+    )

examples/WTMetaD_paper/r1/al_wtmetad/ch3cl_f.xyz

@@ -0,0 +1,8 @@
+6
+
+C          0.11150        0.23091        0.58288
+F         -0.88850        1.32101       -2.02704
+Cl         0.64512       -0.39439        2.08233
+H          1.02099        0.50449       -0.09172
+H         -0.45901        1.13911        0.74042
+H         -0.45901       -0.53348        0.04182

examples/WTMetaD_paper/r1/al_wtmetad/training.py

@@ -0,0 +1,39 @@
+import mlptrain as mlt
+
+mlt.Config.n_cores = 20
+mlt.Config.orca_keywords = ['PBE0', 'def2-SVP', 'EnGrad', 'CPCM(Water)']
+
+if __name__ == '__main__':
+    system = mlt.System(
+        mlt.Molecule('ch3cl_f.xyz', charge=-1, mult=1), box=None
+    )
+
+    # Define CV and attach an upper wall
+    avg_r = mlt.PlumedAverageCV(name='avg_r', atom_groups=((0, 1), (0, 2)))
+    avg_r.attach_upper_wall(location=2.5, kappa=1000)
+
+    # Define CVs for extra information
+    r_f = mlt.PlumedAverageCV(name='r_f', atom_groups=(0, 1))
+    r_cl = mlt.PlumedAverageCV(name='r_cl', atom_groups=(0, 2))
+
+    # Define CV for WTMetaD AL (r_cl - r_f)
+    diff_r = mlt.PlumedDifferenceCV(
+        name='diff_r', atom_groups=((0, 2), (0, 1))
+    )
+
+    # Initialise PlumedBias for WTMetaD AL
+    bias = mlt.PlumedBias(cvs=(avg_r, r_f, r_cl, diff_r))
+    bias.initialise_for_metad_al(width=0.05, cvs=diff_r, biasfactor=100)
+
+    # Define the potential and train using WTMetaD AL (inherit_metad_bias=True)
+    ace = mlt.potentials.ACE('r1_wtmetad', system=system)
+    ace.al_train(
+        method_name='orca',
+        temp=300,
+        n_init_configs=5,
+        n_configs_iter=5,
+        max_active_iters=50,
+        min_active_iters=30,
+        inherit_metad_bias=True,
+        bias=bias,
+    )

examples/WTMetaD_paper/r2/al_downhill/al_downhill.py

@@ -0,0 +1,35 @@
+import autode as ade
+import mlptrain as mlt
+
+# ORCA PATH (change accordingly)
+ade.Config.ORCA.path = '/usr/local/orca_5_0_3/orca'
+
+mlt.Config.n_cores = 10
+mlt.Config.orca_keywords = ['PBE0', 'D3BJ', 'def2-SVP', 'EnGrad']
+
+if __name__ == '__main__':
+    system = mlt.System(mlt.Molecule('ts_r2.xyz', charge=0, mult=1), box=None)
+
+    # Define CVs for extra information
+    avg_r = mlt.PlumedAverageCV(name='avg_r', atom_groups=((14, 11), (14, 10)))
+    r_1 = mlt.PlumedAverageCV(name='r_1', atom_groups=(14, 11))
+    r_2 = mlt.PlumedAverageCV(name='r_2', atom_groups=(14, 10))
+    diff_r = mlt.PlumedDifferenceCV(
+        name='diff_r', atom_groups=((14, 11), (14, 10))
+    )
+
+    # Initialise PlumedBias
+    bias = mlt.PlumedBias(cvs=(avg_r, r_1, r_2, diff_r))
+
+    # Define the potential and train using Downhill AL (fix_init_config=True)
+    ace = mlt.potentials.ACE('r2_downhill', system=system)
+    ace.al_train(
+        method_name='orca',
+        temp=500,
+        n_init_configs=10,
+        n_configs_iter=10,
+        max_active_iters=50,
+        min_active_iters=20,
+        fix_init_config=True,
+        bias=bias,
+    )

examples/WTMetaD_paper/r2/al_downhill/ts_r2.xyz

@@ -0,0 +1,25 @@
+23
+Coordinates from ORCA-job ts_2
+  C   -2.66439005909114      2.65299207388647      0.78745998436255
+  C   -2.67887404989887      1.28747390765675      0.38852882506916
+  C   -1.56970666815882      0.67461786371524     -0.14641426702188
+  C   -0.37799042894344      1.42925812930736     -0.32382038603211
+  C   -0.37079634676906      2.81148083306055      0.09023385565607
+  C   -1.52125738464133      3.40795809751788      0.65254942674056
+  H   -1.50289375312190      4.45626539206397      0.96302768555986
+  H   -3.57070364836348      3.09792166406850      1.20441075664968
+  H   -3.60062126935202      0.71302763969736      0.51535519693843
+  H   -1.60258355173816     -0.37766602828367     -0.43967208064813
+  C   0.92456570544541      3.31753505809060     -0.15347633687560
+  C   1.72786411538397      2.23585108850567     -0.72575815854290
+  H   1.22817413754548      4.36380165541877     -0.12613291191324
+  C   0.88824740245950      1.09215485450898     -0.82943542615214
+  C   2.18463150524301      2.44443139726686      1.05085740248514
+  C   2.96854451026492      2.45002472048448     -1.54037911195286
+  H   1.19994619177669      0.12279718501542     -1.21659289133211
+  H   2.69452544821297      2.65557302244572     -2.58626430067515
+  H   3.60551208150717      1.55243185555685     -1.53156185106088
+  H   3.56503062112247      3.29922665297333     -1.17492648923220
+  H   1.49069905669169      1.84916547328124      1.65040376618190
+  H   2.42620003624003      3.40209098690239      1.53339211735138
+  H   3.13306634818493      1.90539647685923      0.90501519444448

examples/WTMetaD_paper/r2/al_wtmetad/al_wtmetad.py

@@ -0,0 +1,37 @@
+import mlptrain as mlt
+
+mlt.Config.n_cores = 10
+mlt.Config.orca_keywords = ['PBE0', 'D3BJ', 'def2-SVP', 'EnGrad']
+
+if __name__ == '__main__':
+    system = mlt.System(mlt.Molecule('r_r2.xyz', charge=0, mult=1), box=None)
+
+    # Define CV and attach an upper wall
+    avg_r = mlt.PlumedAverageCV(name='avg_r', atom_groups=((14, 11), (14, 10)))
+    avg_r.attach_upper_wall(location=2.5, kappa=1000)
+
+    # Define CVs for extra information
+    r_1 = mlt.PlumedAverageCV(name='r_1', atom_groups=(14, 11))
+    r_2 = mlt.PlumedAverageCV(name='r_2', atom_groups=(14, 10))
+
+    # Define CV for WTMetaD AL (r_1 - r_2)
+    diff_r = mlt.PlumedDifferenceCV(
+        name='diff_r', atom_groups=((14, 11), (14, 10))
+    )
+
+    # Initialise PlumedBias for WTMetaD AL
+    bias = mlt.PlumedBias(cvs=(avg_r, r_1, r_2, diff_r))
+    bias.initialise_for_metad_al(width=0.05, cvs=diff_r, biasfactor=90)
+
+    # Define the potential and train using WTMetaD AL (inherit_metad_bias=True)
+    ace = mlt.potentials.ACE('isoindene_2_metad_2', system=system)
+    ace.al_train(
+        method_name='orca',
+        temp=300,
+        n_init_configs=5,
+        n_configs_iter=10,
+        max_active_iters=40,
+        min_active_iters=20,
+        inherit_metad_bias=True,
+        bias=bias,
+    )

examples/WTMetaD_paper/r2/al_wtmetad/r_r2.xyz

@@ -0,0 +1,25 @@
+23
+Coordinates from ORCA-job reactant_2
+  C   -2.81578879620079      2.71939195713069      0.40597652743383
+  C   -2.75306801675332      1.29879637349104      0.12494353708397
+  C   -1.57543956286648      0.67007495572890     -0.12725319077628
+  C   -0.35564336599371      1.43708094671903     -0.11453115671888
+  C   -0.41953214675013      2.88547229814566      0.17378132549330
+  C   -1.69985337085700      3.49383364072311      0.43197139067454
+  H   -1.76038527053415      4.56397504901440      0.64444557198421
+  H   -3.79415372095779      3.16615899050664      0.60025014318963
+  H   -3.68677075140821      0.73077207382657      0.11837866310995
+  H   -1.54169132169281     -0.40154984311682     -0.33807153245421
+  C   0.83993724079311      3.39615214471678      0.13487848344868
+  C   1.82185831002678      2.30200252248213     -0.18701816957288
+  H   1.13022181850322      4.43459180951911      0.30501970498636
+  C   0.94166848737729      1.08930300007155     -0.32657183576441
+  C   2.83234594843814      2.11612229365514      0.95847709653150
+  C   2.56148744849290      2.59927910422309     -1.50322929151805
+  H   1.32235111859832      0.09373371278190     -0.56213839383041
+  H   1.84979611856880      2.73365377204159     -2.33076142860886
+  H   3.24183470046471      1.77249061784375     -1.76025485532520
+  H   3.16292499408300      3.51688104975674     -1.40914717372226
+  H   2.31566211120603      1.90230302413868      1.90547216568181
+  H   3.43825007798258      3.02608548619431      1.09016398584808
+  H   3.51580406856637      1.28144540475756      0.73822666157863