CGCNN2
As the original Crystal Graph Convolutional Neural Networks (CGCNN) repository is no longer actively maintained, this repository is a reproduction of CGCNN by Xie et al.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 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:
pip install cgcnn2
If 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
Get Started
cgcnn-ft --help
References
The original paper describes the details of the CGCNN framework:
@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}
}
1 maintainer
Metadata
1 maintainer
Release files for cgcnn2 0.3.2
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