Graph comparison of molecular crystals in band gap prediction using neural networks

Abstract

In materials informatics, the representation of the material structure is fundamentally essential to obtain better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but the comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the crystal graph reduces the error 0.4 times with medium correlation than the molecular graph. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of intermolecular interactions in the band gap.