Prediction of mechanical properties of crystal structures using graph neural networks

Abstract

Materials Informatics (MI) is attracting attention as a method to accelerate the design and discovery of new materials by utilizing various material databases and machine learning. In particular, it enables prediction of material properties and efficient search for candidate materials, and is being applied in a wide range of fields such as energy and electronic devices. On the other hand, in the fields of civil engineering, construction, and machinery, mechanical properties such as elastic modulus and corrosion resistance are required as material properties, but material search techniques targeting these properties have not been fully established. In this study, we attempted to apply several existing graph neural network (GNN) models that have been developed for predicting the band gap of crystal structures, especially the volume elastic modulus. The validation results show that all the models have high prediction performance within the scope of this study.