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Allow converting NB potentials with no exclusions #111

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Jul 17, 2024
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Allow converting NB potentials with no exclusions #111

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29 changes: 22 additions & 7 deletions smee/converters/openff/nonbonded.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ def convert_nonbonded_handlers(
v_site_maps: list[smee.VSiteMap | None],
parameter_cols: tuple[str, ...],
attribute_cols: tuple[str, ...] | None = None,
has_exclusions: bool = True,
) -> tuple[smee.TensorPotential, list[smee.NonbondedParameterMap]]:
"""Convert a list of SMIRNOFF non-bonded handlers into a tensor potential and
associated parameter maps.
Expand All @@ -45,6 +46,7 @@ def convert_nonbonded_handlers(
parameter_cols: The ordering of the parameter array columns.
attribute_cols: The handler attributes to include in the potential *in addition*
to the intra-molecular scaling factors.
has_exclusions: Whether the handlers are excepted to define exclusions.

Returns:
The potential containing tensors of the parameter values, and a list of
Expand All @@ -55,11 +57,20 @@ def convert_nonbonded_handlers(
assert len(topologies) == len(handlers), "topologies and handlers must match"
assert len(v_site_maps) == len(handlers), "v-site maps and handlers must match"

if has_exclusions:
attribute_cols = (
"scale_12",
"scale_13",
"scale_14",
"scale_15",
*attribute_cols,
)

potential = smee.converters.openff._openff._handlers_to_potential(
handlers,
handler_type,
parameter_cols,
("scale_12", "scale_13", "scale_14", "scale_15", *attribute_cols),
attribute_cols,
)

parameter_key_to_idx = {
Expand Down Expand Up @@ -107,12 +118,16 @@ def convert_nonbonded_handlers(
for parameter_idx, count in assignment_map[particle_idx].items():
assignment_matrix[particle_idx, parameter_idx] = count

exclusion_to_scale = smee.utils.find_exclusions(topology, v_site_map)
exclusions = torch.tensor([*exclusion_to_scale])
exclusion_scale_idxs = torch.tensor(
[[attribute_to_idx[scale]] for scale in exclusion_to_scale.values()],
dtype=torch.int64,
)
if has_exclusions:
exclusion_to_scale = smee.utils.find_exclusions(topology, v_site_map)
exclusions = torch.tensor([*exclusion_to_scale])
exclusion_scale_idxs = torch.tensor(
[[attribute_to_idx[scale]] for scale in exclusion_to_scale.values()],
dtype=torch.int64,
)
else:
exclusions = torch.zeros((0, 2), dtype=torch.int64)
exclusion_scale_idxs = torch.zeros((0, 1), dtype=torch.int64)

parameter_map = smee.NonbondedParameterMap(
assignment_matrix=assignment_matrix.to_sparse(),
Expand Down
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