An electronic-structure informatics study on hole-transport materials for perovskite solar cells: Molecular design of highly efficient phthalocyanine derivatives

Md. Althaf Hussain; Kazuki Yoshida; Takafumi Inoue; Manabu Sugimoto

43th Symposium on Chemoinformatics

Conference information

Host: Division of Chemoinformatics, The Chemical Society of Japan

Co-host: Data Science Center, Nara Institue of Science and Technology

Name : Symposium on Chemoinformatics

Number : 43

Location : [in Japanese]

Date : December 09, 2020 -

Oral Session

An electronic-structure informatics study on hole-transport materials for perovskite solar cells: Molecular design of highly efficient phthalocyanine derivatives

*Md. Althaf Hussain, Kazuki Yoshida, Takafumi Inoue, Manabu Sugimoto

Author information

Keywords: machine learning, regression model, hole mobility, electronic-structure informatics

CONFERENCE PROCEEDINGS FREE ACCESS

Pages 1A11-

Details

Abstract

Machine learning was applied to derive regression models for predicting hole mobility of hole-transporting phthalocyanine derivatives. In the analysis, the descriptors were numerically evaluated by using quantum chemical calculations.

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