This repository contains an implementation of the SGCNN (Slab Graph Convolutional Neural Network) that predicts surface-related properties of crystal structures.
It provides 1) training a SGCNN model 2) an easy use to prediction of an adsorption energy using pretrained model.
This project required TensorFlow > 1.7.0
Clone this repository:
git clone https://github.com/myungjoon/SGCNN.git
Training consists of two steps. First you need to prepare your dataset. After that, instill the dataset into SGCNN structure implemented by TensorFlow.
SGCNN takes input as bulk and surface crystal graphs. Currently, this software automatically converts POSCAR format file to the crystal graphs via running cgsurface.py and cgbulk.py.
To apply your dataset, you need to prepare two POSCAR files with same name in different directories, surface/ and bulk/.
- Our model read POSCAR files. If you have CIF files, you should convert it to POSCAR file.
You can add your own features to feature.csv file.
You need to write data.txt file for the dataset. Our sgcnn.py file will read 'data.txt' file, and input graphs and output values (binding energies) are extracted.
'data.txt' file has following format.
#adsorbate crystal face site atom1 atom2 adsorption energy
1 1 111 11 Au Ti -0.87
4 1 111 21 Au Ti -3.931
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Each row describes a structure, composition, and surface-related property (adsorption energy)
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Each column represents specific characteristic.
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Corresponding POSCAR files should be stored in
bulk/andsurface/directories.
The trained model will be saved as 'models/best.ckpt'
You can use your own model by running test.py. This python file reads 'test.txt' and writes results on 'result.txt'.
To use pretrained model for predictions of adsorption energy, you can simply use pretrained.py. This python file reads 'test.txt' and writes results on 'result.txt'.
If you use SGCNN, please cite us using
@article{Kim2020,
author = {Kim, Myungjoon and Yeo, Byung Chul and Park, Youngtae and Lee, Hyuck Mo and Han, Sang Soo and Kim, Donghun},
title = {Artificial Intelligence to Accelerate the Discovery of N2 Electroreduction Catalysts},
journal = {Chemistry of Materials},
volume = {32},
number = {2},
pages = {639-912},
year = {2020},
doi = {10.1021/acs.chemmater.9b03686}
}