boosting-CGCNN

Implementation codes for Boosting-Crystal Graph Convolutional Neural Network (Boosting-CGCNN) Link.
(Dataset used in the training can be found in https://github.com/sunilegna/GB-CGCNN which contains the same content as this repository.)

Installation

1. Make a conda environment

conda create -n gb python=3.9
conda activate gb

2. Install dependencies

git clone https://github.com/sunilegna/GB-CGCNN.git
pip install poetry
poetry install

3. Training code

python main.py

How to Cite

If you use this code, please reference the following paper:

1. Eun Ho Kim, Jun Hyeong Gu, June Ho Lee, Seong Hun Kim, Jaeseon Kim, Hyo Gyeong Shin, Shin Hyun Kim, Donghwa Lee. Boosting–crystal graph convolutional neural network for predicting highly imbalanced data: a case study for metal-insulator transition materials. ACS Appl. Mater. Interfaces, 16(33), 43734-43741 (2024). https://pubs.acs.org/doi/10.1021/acsami.4c07851