Metadata-Version: 2.3
Name: cgcnn2
Version: 0.3.0
Summary: Crystal Graph Convolutional Neural Networks
License: MIT
Keywords: python,gnn,vasp,crystal
Author: Jiacheng Wang
Author-email: jiachengwang@umass.edu
Maintainer: Jiacheng Wang
Requires-Python: >=3.10,<3.13
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: ase (>=3.23.0,<4.0.0)
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pymatgen (>=2025.1.0,<2026.0.0)
Requires-Dist: pymatviz (>=0.15.0,<0.16.0)
Requires-Dist: scikit-learn
Requires-Dist: torch (>=2.5.1,<3.0.0)
Project-URL: Homepage, https://github.com/jcwang587/cgcnn2/
Project-URL: Repository, https://github.com/jcwang587/cgcnn2/
Description-Content-Type: text/markdown

# CGCNN2

As the original Crystal Graph Convolutional Neural Networks (CGCNN) repository is no longer actively maintained, this repository is a reproduction of [CGCNN](https://github.com/txie-93/cgcnn) by Xie et al. It includes necessary updates for deprecated components and a few additional functions to ensure smooth operation. Despite its age, CGCNN remains a straightforward and fast deep learning framework that is easy to learn and use.

The package provides following major functions:

- **Training** a CGCNN model with a customized dataset.
- **Predicting** material properties with a pre-trained CGCNN model.
- **Fine-tuning** a pre-trained CGCNN model on a new dataset.
- **Extracting** atomic features as descriptors for the downstream task.

## Installation

Make sure you have a Python interpreter, preferably version 3.10 or higher. Then, you can simply install xdatbus from
PyPI using `pip`:

```bash
pip install cgcnn2
```

If you'd like to use the latest unreleased version on the main branch, you can install it directly from GitHub:

```bash
pip install git+https://github.com/jcwang587/cgcnn2
```

## Get Started

```bash
cgcnn-ft --help
```


## References

The original paper describes the details of the CGCNN framework:

```bibtex
@article{PhysRevLett2018,
  title = {Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties},
  author = {Xie, Tian and Grossman, Jeffrey C.},
  journal = {Phys. Rev. Lett.},
  volume = {120},
  issue = {14},
  pages = {145301},
  numpages = {6},
  year = {2018},
  month = {Apr},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.120.145301},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.145301}
}
```

