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http://dx.doi.org/10.4313/JKEM.2022.35.2.4

Prediction of Material's Formation Energy Using Crystal Graph Convolutional Neural Network  

Lee, Hyun-Gi (School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology(UNIST))
Seo, Dong-Hwa (School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology(UNIST))
Publication Information
Journal of the Korean Institute of Electrical and Electronic Material Engineers / v.35, no.2, 2022 , pp. 134-142 More about this Journal
Abstract
As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.
Keywords
Crystal graph convolutional neural network; Machine learning; Deep learning; Artificial intelligence; Convolutional neural network;
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