[14p-PB7-1~29] 12.3 Functional Materials and Novel Devices

[14p-PB7-9] Prediction of Physical Properties for Organic Semiconductor using Machine Learning

〇Masahito Segawa¹, Kazuki Mori¹ (1.CTC)

Keywords:Organic Semiconductor, mashine learning, band gap

In order to search for semiconductor materials with better physical properties, we will verify the usefulness of the calculation and machine learning using a Web-based database and cloud computer. In this study, we predicted the band gap of polythiophene-based material using volume, density, proportion of each atom, similarity of each structure, electric charge MolLogP and TPSA as feature value. By creating a large number of structures, we aim to search for new materials that have desirable values of electrical conductivity and the like.