# cace

**Repository Path**: pfsuo/cace

## Basic Information

- **Project Name**: cace
- **Description**: CACE from github
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-06
- **Last Updated**: 2025-10-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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

```text
@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.