Metadata-Version: 2.1
Name: cgcnn2
Version: 0.0.4
Summary: Crystal Graph Convolutional Neural Networks
Home-page: https://github.com/jcwang587/cgcnn2/
License: MIT
Keywords: python,aimd,vasp,gnn
Author: Jiacheng Wang
Author-email: jiachengwang@umass.edu
Maintainer: Jiacheng Wang
Requires-Python: >=3.10,<3.12
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
Requires-Dist: mdanalysis
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pymatgen
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As the original Crystal Graph Convolutional Neural Networks (CGCNN) repository is no longer actively maintained, this repository is a reproduction of [CGCNN](https://github.com/txie-93/cgcnn) by Xie et al.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 two major functions:

- **Training** a CGCNN model with a customized dataset.
- **Predicting** material properties with a pre-trained CGCNN model.

The original paper describes the details of the CGCNN framework:

[Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties](https://link.aps.org/doi/10.1103/PhysRevLett.120.145301)

