tfep.potentials.gromacs.gromacs_potential_energy

tfep.potentials.gromacs.gromacs_potential_energy(batch_positions: Tensor, batch_cell: Tensor, tpr_file_path: str, launcher: Launcher | None = None, positions_unit: Unit | None = None, energy_unit: Unit | None = None, precompute_gradient: bool = True, working_dir_path: str | List[str] | None = None, cleanup_working_dir: bool = False, parallelization_strategy: ParallelizationStrategy | None = None, launcher_kwargs: Dict[str, Any] | None = None, mdrun_kwargs: Dict[str, Any] | None = None, on_mdrun_error: Literal['raise', 'nan'] = 'raise')[source]

PyTorch-differentiable QM/MM potential energy using GROMACS.

PyTorch ``Function``s do not accept keyword arguments. This function wraps GROMACSPotentialEnergyFunc.apply() to enable standard functional notation. See the documentation on the original function for the input parameters.