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Only do gradient contribution calculation if the tabulator has interaction type #302

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Merged
mphoward merged 5 commits into from
Jun 25, 2025
Merged

Only do gradient contribution calculation if the tabulator has interaction type #302

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673ed23
Check if tabulator has interaction type before doing gradient calcula…
clpetix May 15, 2025
6b247bf
Add checks for non-zero bond, angle, and dihedral counts in gradient …
clpetix Jun 4, 2025
955b985
Fix typo in check.
clpetix Jun 4, 2025
b226549
Add test with no bonded potetntials.
clpetix Jun 4, 2025
069c40e
Merge branch 'main' into fix/direct-average-contribution-condition
mphoward Jun 25, 2025
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29 changes: 15 additions & 14 deletions src/relentless/optimize/objective.py
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Original file line number Diff line number Diff line change
Expand Up @@ -585,21 +585,22 @@ def _calc_ensemble_average_dvar_gradient(self, trajectory, variables):
filter_j_gt_i = neighbors[:, 1] > neighbors[:, 0]
neighbors.filter(filter_j_gt_i)
# pair contributions to the gradient
for i in self.potentials.pair.types:
for j in self.potentials.pair.types:
filter_ij = numpy.logical_and(
type_masks[i][neighbors[:, 0]],
type_masks[j][neighbors[:, 1]],
)
for var in variables:
gradient[var] += numpy.sum(
self.potentials.pair.derivative(
(i, j), var, x=neighbors.distances[filter_ij]
)
if self.potentials.pair is not None:
for i in self.potentials.pair.types:
for j in self.potentials.pair.types:
filter_ij = numpy.logical_and(
type_masks[i][neighbors[:, 0]],
type_masks[j][neighbors[:, 1]],
)
for var in variables:
gradient[var] += numpy.sum(
self.potentials.pair.derivative(
(i, j), var, x=neighbors.distances[filter_ij]
)
)

# bond contributions to the gradient
if snap.bonds.N != 0:
if self.potentials.bond is not None:
bond_type_map = {
type: i for i, type in enumerate(self.potentials.bond.types)
}
Expand All @@ -624,7 +625,7 @@ def _calc_ensemble_average_dvar_gradient(self, trajectory, variables):
)

# angle contributions to the gradient
if snap.angles.N != 0:
if self.potentials.angle is not None:
angle_type_map = {
type: i for i, type in enumerate(self.potentials.angle.types)
}
Expand Down Expand Up @@ -657,7 +658,7 @@ def _calc_ensemble_average_dvar_gradient(self, trajectory, variables):
)

# dihedral contributions to the gradient
if snap.dihedrals.N != 0:
if self.potentials.dihedral is not None:
dihedral_type_map = {
type: i for i, type in enumerate(self.potentials.dihedral.types)
}
Expand Down