Cartesian Atomic Cluster Expansion for Machine Learning Interatomic Potentials (CACE)

Summary

The Cartesian Atomic Cluster Expansion (CACE) is a new approach for developing machine learning interatomic potentials. This method utilizes Cartesian coordinates to provide a complete description of atomic environments, maintaining interaction body orders. It integrates low-dimensional embeddings of chemical elements with inter-atomic message passing.

Requirements

• Python 3.6 or higher
• NumPy
• ASE (Atomic Simulation Environment)
• PyTorch
• matscipy

Installation

Please refer to the setup.py file for installation instructions.

Usage

Please refer to the scripts/train.py.

More example scripts can be found in [https://github.com/BingqingCheng/cacefit].

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

@article{cheng2024cartesian,
  title={Cartesian atomic cluster expansion for machine learning interatomic potentials},
  author={Cheng, Bingqing},
  journal={npj Computational Materials},
  volume={10},
  number={1},
  pages={157},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

Contact

For any queries regarding CACE, please contact Bingqing Cheng at tonicbq@gmail.com.