Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks

Takuya Taniguchi; Mayuko Hosokawa; Toru Asahi

doi:10.1021/acsomega.3c05224

Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks

ACS Omega. 2023 Oct 16;8(42):39481-39489. doi: 10.1021/acsomega.3c05224. eCollection 2023 Oct 24.

Authors

Takuya Taniguchi¹, Mayuko Hosokawa², Toru Asahi²

Affiliations

¹ Center for Data Science, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan.
² Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-Ku, Tokyo 169-8555, Japan.

Abstract

In material informatics, the representation of the material structure is fundamentally essential to obtaining 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 a 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 obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap.