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Implement calculating and dumping deviation between DeepMD models #6

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Feb 21, 2024
Merged

Implement calculating and dumping deviation between DeepMD models #6

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20 changes: 20 additions & 0 deletions emle/calculator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -376,6 +376,7 @@ def __init__(
plugin_path=".",
mm_charges=None,
deepmd_model=None,
deepmd_deviation=None,
rascal_model=None,
parm7=None,
qm_indices=None,
Expand Down Expand Up @@ -437,6 +438,10 @@ def __init__(
Path to the DeePMD model file to use for in vacuo calculations. This
must be specified if "deepmd" is the selected backend.

deepmd_deviation: str
Path to the file to dump max deviation between forces predicted
with the deepmd models.

rascal_model: str
Path to the Rascal model file used to apply delta-learning corrections
to the in vacuo energies and gradients computed by the backed.
Expand Down Expand Up @@ -745,6 +750,11 @@ def __init__(

# Store the list of model files, removing any duplicates.
self._deepmd_model = list(set(deepmd_model))
if len(self._deepmd_model == 1 and deepmd_deviation):
msg = "More that one deepmd model needed to calculate deviation"
_logger.error(msg)
raise IOError(msg)
self.deepmd_deviation = deepmd_deviation

# Initialise DeePMD backend attributes.
try:
Expand Down Expand Up @@ -1139,6 +1149,7 @@ def __init__(
"external_backend": None if external_backend is None else external_backend,
"mm_charges": None if mm_charges is None else self._mm_charges.tolist(),
"deepmd_model": deepmd_model,
"deepmd_deviation": deepmd_deviation,
"rascal_model": rascal_model,
"parm7": parm7,
"qm_indices": None if qm_indices is None else self._qm_indices,
Expand Down Expand Up @@ -2666,6 +2677,8 @@ def _run_deepmd(self, xyz, elements):
# Reshape to a frames x (natoms x 3) array.
xyz = xyz.reshape([1, -1])

f_list = []

# Run a calculation for each model and take the average.
for x, dp in enumerate(self._deepmd_potential):
# Work out the mapping between the elements and the type indices
Expand All @@ -2682,10 +2695,17 @@ def _run_deepmd(self, xyz, elements):

if x == 0:
energy, force, _ = dp.eval(xyz, cells=None, atom_types=atom_types)
f_list.append(force[0])
else:
e, f, _ = dp.eval(xyz, cells=None, atom_types=atom_types)
energy += e
force += f
f_list.append(f[0])

if self.deepmd_deviation:
max_f_std = _np.max(_np.std(_np.array(f_list), axis=0))
with open(self.deepmd_deviation, 'a') as deepmd_dev_file:
deepmd_dev_file.write(f'{max_f_std:12.5f}\n')

# Take averages and return. (Gradient equals minus the force.)
return (
Expand Down
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