deepmd_gnn.mace_off#

Conservative helpers for loading selected MACE-OFF checkpoints.

The helpers in this module intentionally support a narrower scope than the upstream discussion in PR #96: they load ordinary MACE-OFF checkpoints into a DeePMD-GNN MaceModel wrapper for standard MD use, but they do not infer DPRc/QM/MM atom-type semantics from the checkpoint.

Functions#

`download_mace_off_model`(→ pathlib.Path)	Download a selected official MACE-OFF model file.
`load_mace_off_model`(→ deepmd_gnn.mace.MaceModel)	Load a supported MACE-OFF checkpoint as a DeePMD-GNN `MaceModel`.
`convert_mace_off_to_deepmd`(→ pathlib.Path)	Serialize a loaded MACE-OFF wrapper as a TorchScript DeePMD-GNN model.

Module Contents#

deepmd_gnn.mace_off.download_mace_off_model(model_name: str, cache_dir: pathlib.Path | None = None) → pathlib.Path[source]#

Download a selected official MACE-OFF model file.

Parameters#

model_name: Canonical model name (for example off23_small) or one of the compatibility aliases small / medium / large.
cache_dir: Cache directory. Defaults to get_mace_off_cache_dir().

deepmd_gnn.mace_off.load_mace_off_model(model_name: str | None = 'small', *, sel: int, model_path: pathlib.Path | None = None, cache_dir: pathlib.Path | None = None, device: str = 'cpu') → deepmd_gnn.mace.MaceModel[source]#

Load a supported MACE-OFF checkpoint as a DeePMD-GNN MaceModel.

Parameters#

model_name: Name of the official MACE-OFF checkpoint to download. Ignored when model_path is provided.
sel: Neighbor-list cap for the DeePMD-GNN wrapper. This value is required: MACE-OFF checkpoints do not store DeePMD’s runtime neighbor-list limit, and guessing it can silently truncate neighbors.
model_path: Local checkpoint path. If omitted, download the selected official model.
cache_dir: Cache directory for downloaded checkpoints.
device: Device used when loading the original checkpoint.

Notes#

This helper intentionally supports a narrower scope than DeePMD-GNN’s full DPRc machinery. It only infers ordinary element type_map entries from atomic_numbers and does not infer mH / HW / OW or other QM/MM-specific type semantics.

deepmd_gnn.mace_off.convert_mace_off_to_deepmd(output_file: str, *, sel: int, model_name: str | None = 'small', model_path: pathlib.Path | None = None, cache_dir: pathlib.Path | None = None, device: str = 'cpu') → pathlib.Path[source]#

Serialize a loaded MACE-OFF wrapper as a TorchScript DeePMD-GNN model.

This helper scripts the DeePMD-GNN wrapper and writes it to output_file. It does not, by itself, prove end-to-end downstream deployment in external engines such as LAMMPS or AMBER; callers should still validate the final deployment path they care about.