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import torch
from ase import Atoms
from ase.calculators.calculator import all_changes
from huggingface_hub import hf_hub_download
from torch_geometric.data import Data
from mlip_arena.models import MLIPCalculator
class MACE_MP_Medium(MLIPCalculator):
def __init__(
self,
device: torch.device | None = None,
restart=None,
atoms=None,
directory=".",
**kwargs,
):
self.device = device or torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
)
fpath = hf_hub_download(
repo_id="cyrusyc/mace-universal",
subfolder="pretrained",
filename="2023-12-12-mace-128-L1_epoch-199.model",
revision="main",
)
model = torch.load(fpath, map_location=self.device)
super().__init__(
model=model, restart=restart, atoms=atoms, directory=directory, **kwargs
)
self.name: str = self.__class__.__name__
self.implemented_properties = ["energy", "forces", "stress"]
def calculate(
self, atoms: Atoms, properties: list[str], system_changes: list = all_changes
):
"""Calculate energies and forces for the given Atoms object"""
super().calculate(atoms, properties, system_changes)
output = self.forward(atoms)
self.results = {}
if "energy" in properties:
self.results["energy"] = output["energy"].item()
if "forces" in properties:
self.results["forces"] = output["forces"].cpu().detach().numpy()
if "stress" in properties:
self.results["stress"] = output["stress"].cpu().detach().numpy()
def forward(self, x: Data | Atoms) -> dict[str, torch.Tensor]:
"""Implement data conversion, graph creation, and model forward pass"""
# TODO
raise NotImplementedError
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