Materials Informatics of Molecular Crystals Compared by Molecular and Crystal Representations: The Case of Band Gap Prediction

• Published in: ChemRxiv
• Main Authors: Taniguchi, Takuya, Hosokawa, Mayuko, Asahi, Toru
• Format: Paper
• Language: English
• Published: Washington American Chemical Society 20.10.2022
• Subjects: Chemical fingerprinting; Crystals; Energy gap; Machine learning; Materials information; Organic semiconductors; Photovoltaic cells; Representations; Screening
• ISSN: 2573-2293
• DOI: 10.26434/chemrxiv-2022-q426t

In materials informatics, the representation of materialstructures is fundamentally important to obtain better prediction results. Molecular crystals can be represented by both molecular and crystal representations, but there has been no examination to determine which representation is the most effective for the materials informatics of molecular crystals. In this work, different representations for molecular crystals were compared in an exemplified task of band gap prediction. We demonstrated that thepredictive ability using molecular graph outperformed those of molecular fingerprints and crystal graphs. This result motivated the screening of molecular big data from PubChem, and the inference suggested candidate molecules of organic semiconductors for photovoltaics and luminescence. The novelty of this work relies on the representation comparison of molecular crystals and the finding that molecular graph works better even though the property prediction of crystalline materials. This finding will enable to machine-learning-aided screening and design of functional molecular crystals.