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MACE models
Effortlessly integrate pre-trained MACE models into your projects with the user-friendly mace-models
package.
pip install mace-models
Loading models is simple with a few lines of code:
import mace_models
# Load a default MACE model
model = mace_models.load()
torch_model = model.get_model()
ase_calculator = model.get_calculator()
# get the model file
model.get_file()
Customize your model selection by loading models from various repositories or specific revisions:
model = mace_models.load(
"<model-name>",
rev="<branch-or-sha>",
remote="https://github.com/<user>/<repo>"
)
Ensure a seamless integration by first installing the MACE library and upgrading your PyTorch installation:
pip install --upgrade torch --extra-index-url https://download.pytorch.org/whl/cu116
pip install git+https://github.com/ACEsuit/mace.git
References
Models are taken from https://github.com/ACEsuit/mace/blob/docs/docs/examples/ANI_trained_MACE.zip and from
@misc{kovacsMACEOFF23TransferableMachine2023,
title = {MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules},
author = {Kov{\'a}cs, D{\'a}vid P{\'e}ter and Moore, J. Harry and Browning, Nicholas J. and Batatia, Ilyes and Horton, Joshua T. and Kapil, Venkat and Witt, William C. and Magd{\u a}u, Ioan-Bogdan and Cole, Daniel J. and Cs{\'a}nyi, G{\'a}bor},
year = {2023},
number = {arXiv:2312.15211},
}
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