/GCNEnergy

Primary LanguagePythonMIT LicenseMIT

GCN Energy

PBE energy & Bandgap Prediction by GCN models (Train from QMOF Database)

Requires Python 3.9 Zenodo MIT Gmail Linux Windows

Installation

Download

git clone https://github.com/sxm13/GCNEnergy.git
cd GCNEnergy
pip install -r requirements.txt

Energy Prediction

python GCNEnergy.py folder-name[path]
  • folder-name: relative path to a folder with cif files without partial atomic charges

Reference

If you use GCN Energy, please consider citing this paper:

@article{,
    title={PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials based on Crystal Graph Convolution Network},
    DOI={10.1021/acs.jctc.4c00434},
    journal={Journal of Chemical Theory and Computation},
    author={Zhao, Guobin and Chung, Yongchul},
    year={2024}
}