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import os
import urllib
from typing import Literal
import matgl
import torch
from alignn.ff.ff import AlignnAtomwiseCalculator, get_figshare_model_ff
from ase import Atoms
from chgnet.model.dynamics import CHGNetCalculator
from chgnet.model.model import CHGNet as CHGNetModel
from fairchem.core import OCPCalculator
from mace.calculators import MACECalculator
from matgl.ext.ase import PESCalculator
from orb_models.forcefield import pretrained
from orb_models.forcefield.calculator import ORBCalculator
from sevenn.sevennet_calculator import SevenNetCalculator
# Avoid circular import
def get_freer_device() -> torch.device:
"""Get the GPU with the most free memory, or use MPS if available.
s
Returns:
torch.device: The selected GPU device or MPS.
Raises:
ValueError: If no GPU or MPS is available.
"""
device_count = torch.cuda.device_count()
if device_count > 0:
# If CUDA GPUs are available, select the one with the most free memory
mem_free = [
torch.cuda.get_device_properties(i).total_memory
- torch.cuda.memory_allocated(i)
for i in range(device_count)
]
free_gpu_index = mem_free.index(max(mem_free))
device = torch.device(f"cuda:{free_gpu_index}")
print(
f"Selected GPU {device} with {mem_free[free_gpu_index] / 1024**2:.2f} MB free memory from {device_count} GPUs"
)
elif torch.backends.mps.is_available():
# If no CUDA GPUs are available but MPS is, use MPS
print("No GPU available. Using MPS.")
device = torch.device("mps")
else:
# Fallback to CPU if neither CUDA GPUs nor MPS are available
print("No GPU or MPS available. Using CPU.")
device = torch.device("cpu")
return device
class MACE_MP_Medium(MACECalculator):
def __init__(self, device=None, default_dtype="float32", **kwargs):
checkpoint_url = "http://tinyurl.com/5yyxdm76"
cache_dir = os.path.expanduser("~/.cache/mace")
checkpoint_url_name = "".join(
c for c in os.path.basename(checkpoint_url) if c.isalnum() or c in "_"
)
cached_model_path = f"{cache_dir}/{checkpoint_url_name}"
if not os.path.isfile(cached_model_path):
os.makedirs(cache_dir, exist_ok=True)
# download and save to disk
print(f"Downloading MACE model from {checkpoint_url!r}")
_, http_msg = urllib.request.urlretrieve(checkpoint_url, cached_model_path)
if "Content-Type: text/html" in http_msg:
raise RuntimeError(
f"Model download failed, please check the URL {checkpoint_url}"
)
print(f"Cached MACE model to {cached_model_path}")
model = cached_model_path
msg = f"Using Materials Project MACE for MACECalculator with {model}"
print(msg)
device = device or str(get_freer_device())
super().__init__(
model_paths=model, device=device, default_dtype=default_dtype, **kwargs
)
class MACE_OFF_Medium(MACECalculator):
def __init__(self, device=None, default_dtype="float32", **kwargs):
checkpoint_url = "https://github.com/ACEsuit/mace-off/raw/main/mace_off23/MACE-OFF23_medium.model?raw=true"
cache_dir = os.path.expanduser("~/.cache/mace")
checkpoint_url_name = "".join(
c for c in os.path.basename(checkpoint_url) if c.isalnum() or c in "_"
)
cached_model_path = f"{cache_dir}/{checkpoint_url_name}"
if not os.path.isfile(cached_model_path):
os.makedirs(cache_dir, exist_ok=True)
# download and save to disk
print(f"Downloading MACE model from {checkpoint_url!r}")
_, http_msg = urllib.request.urlretrieve(checkpoint_url, cached_model_path)
if "Content-Type: text/html" in http_msg:
raise RuntimeError(
f"Model download failed, please check the URL {checkpoint_url}"
)
print(f"Cached MACE model to {cached_model_path}")
model = cached_model_path
msg = f"Using Materials Project MACE for MACECalculator with {model}"
print(msg)
device = device or str(get_freer_device())
super().__init__(
model_paths=model, device=device, default_dtype=default_dtype, **kwargs
)
class CHGNet(CHGNetCalculator):
def __init__(
self,
model: CHGNetModel | None = None,
use_device: str | None = None,
stress_weight: float | None = 1 / 160.21766208,
on_isolated_atoms: Literal["ignore", "warn", "error"] = "warn",
**kwargs,
) -> None:
use_device = use_device or str(get_freer_device())
super().__init__(
model=model,
use_device=use_device,
stress_weight=stress_weight,
on_isolated_atoms=on_isolated_atoms,
**kwargs,
)
def calculate(
self,
atoms: Atoms | None = None,
properties: list | None = None,
system_changes: list | None = None,
) -> None:
super().calculate(atoms, properties, system_changes)
# for ase.io.write compatibility
self.results.pop("crystal_fea", None)
class M3GNet(PESCalculator):
def __init__(
self,
state_attr: torch.Tensor | None = None,
stress_weight: float = 1.0,
**kwargs,
) -> None:
potential = matgl.load_model("M3GNet-MP-2021.2.8-PES")
super().__init__(potential, state_attr, stress_weight, **kwargs)
class EquiformerV2(OCPCalculator):
def __init__(
self,
model_name="EquiformerV2-lE4-lF100-S2EFS-OC22",
local_cache="/tmp/ocp/",
cpu=False,
seed=0,
**kwargs,
) -> None:
super().__init__(
model_name=model_name,
local_cache=local_cache,
cpu=cpu,
seed=0,
**kwargs,
)
def calculate(self, atoms: Atoms, properties, system_changes) -> None:
super().calculate(atoms, properties, system_changes)
self.results.update(
force=atoms.get_forces(),
)
class EquiformerV2OC20(OCPCalculator):
def __init__(
self,
model_name="EquiformerV2-31M-S2EF-OC20-All+MD",
local_cache="/tmp/ocp/",
cpu=False,
seed=0,
**kwargs,
) -> None:
super().__init__(
model_name=model_name,
local_cache=local_cache,
cpu=cpu,
seed=0,
**kwargs,
)
class eSCN(OCPCalculator):
def __init__(
self,
model_name="eSCN-L6-M3-Lay20-S2EF-OC20-All+MD",
local_cache="/tmp/ocp/",
cpu=False,
seed=0,
**kwargs,
) -> None:
super().__init__(
model_name=model_name,
local_cache=local_cache,
cpu=cpu,
seed=0,
**kwargs,
)
def calculate(self, atoms: Atoms, properties, system_changes) -> None:
super().calculate(atoms, properties, system_changes)
self.results.update(
force=atoms.get_forces(),
)
class ALIGNN(AlignnAtomwiseCalculator):
def __init__(self, dir_path: str = "/tmp/alignn/", device=None, **kwargs) -> None:
_ = get_figshare_model_ff(dir_path=dir_path)
device = device or get_freer_device()
super().__init__(path=dir_path, device=device, **kwargs)
class SevenNet(SevenNetCalculator):
def __init__(self, device=None, **kwargs):
device = device or get_freer_device()
super().__init__("7net-0", device=device, **kwargs)
class ORB(ORBCalculator):
def __init__(self, device=None, **kwargs):
device = device or get_freer_device()
orbff = pretrained.orb_v1(device=device)
super().__init__(orbff, device=device, **kwargs)
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