if model is False:
ret_data["error"] = QCEngineError(error_type="input_error",
error_message="run_torchani only accepts the ANI1 method.")
return Result(**{**input_data.dict(), **ret_data})
// Build species
species = "".join(input_data.molecule.symbols)
unknown_sym = set(species) - {"H", "C", "N", "O"}
if unknown_sym:
ret_data["error"] = QCEngineError(error_type="input_error",
error_message="The "{}" model does not support symbols: {}.".format(
input_data.model.method, unknown_sym))
return Result(**{**input_data.dict(), **ret_data})
species = builtin.consts.species_to_tensor(species).to(device).unsqueeze(0)
After Change
if model is False:
ret_data["error"] = ComputeError(error_type="input_error",
error_message="run_torchani only accepts the ANI1 method.")
return FailedOperation(input_data=input_data.dict(), **ret_data)
// Build species
species = "".join(input_data.molecule.symbols)
unknown_sym = set(species) - {"H", "C", "N", "O"}
if unknown_sym:
ret_data["error"] = ComputeError(error_type="input_error",
error_message="The "{}" model does not support symbols: {}.".format(
input_data.model.method, unknown_sym))
return FailedOperation(input_data=input_data.dict(), **ret_data)
species = builtin.consts.species_to_tensor(species).to(device).unsqueeze(0)
// Build coord array
geom_array = input_data.molecule.geometry.reshape(1, -1, 3) * ureg.conversion_factor("bohr", "angstrom")
coordinates = torch.tensor(geom_array.tolist(), requires_grad=True, device=device)
_, energy = model((species, coordinates))
ret_data["properties"] = {"return_energy": energy.item()}
if input_data.driver == "energy":
ret_data["return_result"] = ret_data["properties"]["return_energy"]
elif input_data.driver == "gradient":
derivative = torch.autograd.grad(energy.sum(), coordinates)[0].squeeze()
ret_data["return_result"] = np.asarray(derivative * ureg.conversion_factor("angstrom", "bohr")).ravel().tolist()
else:
ret_data["error"] = ComputeError(error_type="input_error",
error_message="run_torchani did not understand driver method "{}".".format(
input_data.driver))
return FailedOperation(input_data=input_data.dict(), **ret_data)
ret_data["provenance"] = Provenance(
creator="torchani",
version="unknown",
