This repository contains an implementation of the ASGCNN (Adsorbate-Site Graph Convolutional Neural Network) that predicts the adsorption energies with the help of classification tasks for adsorbate types and adsorption sites of slab structures.
- torch
- dgl
- igraph
- sklearn
- pymatgen
- matplotlib
- tqdm
- numpy
- pandas
- qmpy_rester
- ASGCNN/Encoder.py: Generate graph structure from VASP structure file and encode node and edge features.
- ASGCNN/Model.py: Pytorch implementation of the ASGCNN model.
- ASGCNN/Traniner.py: A module that calls the GNN model for training and prediction.
- data: Stores graph structures and targets for network training. Graphs are stored as .bin files in the dgl package.
- figures: Pictures drawn in Python in the article. Some of the drawings require custom scripts. Some code cannot run directly due to data size limitations.
- pretrained: Pretrained models. There are five models learned in an ensemble method, and they predict together to provide the uncertainty of the prediction results.
- structures: VASP structure files for calculation and graph structure generation.
- Query data: Query Heusler alloy data from OQMD: Query_data.ipynb
- Batch construction of adsorption models and analysis of VASP results: This part is done through custom scripts
- Use the pre-trained model or train a new model from scratch: tutorials.ipynb
If you are interested in our work, you can read our literature, and cite us using
@article{ZHOU2024160519,
title = {Machine-learning-accelerated screening of Heusler alloys for nitrogen reduction reaction with graph neural network},
journal = {Applied Surface Science},
volume = {669},
pages = {160519},
year = {2024},
issn = {0169-4332},
doi = {https://doi.org/10.1016/j.apsusc.2024.160519},
url = {https://www.sciencedirect.com/science/article/pii/S0169433224012327},
author = {Jing Zhou and Xiayong Chen and Xiao Jiang and Zean Tian and Wangyu Hu and Bowen Huang and Dingwang Yuan}