Metadata-Version: 2.1
Name: mace-models
Version: 0.1.0
Summary: 
Author: Rokas Elijošius
Author-email: re344@cam.ac.uk
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: ase (>=3.22.1,<4.0.0)
Requires-Dist: dvc-s3 (>=3,<4)
Requires-Dist: zntrack (>=0.7.2,<0.8.0)
Project-URL: repository, https://github.com/RokasEl/MACE-Models
Description-Content-Type: text/markdown

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# MACE models
Effortlessly integrate pre-trained MACE models into your projects with the user-friendly ``mace-models`` package.

```sh
pip install mace-models
```

Loading models is simple with a few lines of code:

```python
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:

```python
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:

```sh
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},
}
```



