As the original Crystal Graph Convolutional Neural Networks (CGCNN) repository is no longer actively maintained, this repository is a reproduction of CGCNN by Xie et al.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 the following major functions:
- Training a CGCNN model using a custom dataset.
- Predicting material properties with a pre-trained CGCNN model.
- Fine-tuning a pre-trained CGCNN model on a new dataset.
- Extracting structural features as descriptors for downstream tasks.
Make sure you have a Python interpreter, preferably version 3.11 or higher. Then, you can simply install cgcnn2 from
PyPI using pip:
pip install cgcnn2If you'd like to use the latest unreleased version on the main branch, you can install it directly from GitHub:
pip install git+https://github.com/jcwang587/cgcnn2@mainThere are entry points for training, predicting, and fine-tuning CGCNN models. For example, to explore the usage of the provided training script cgcnn-tr, you can use the --help option of the command:
cgcnn-tr --helpSimilarly, you can access the predicting and fine-tuning functionalities through cgcnn-pr and cgcnn-ft commands. A detailed user guide documentation is available at: https://jcwang.dev/cgcnn2/
The original paper describes the CGCNN framework in detail:
@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://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301}
}