基于分子指纹及机器学习回归模型的有机光伏材料效率预测

doi:10.11896/cldb.20020113

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Abstract The development of organic photovoltaics (OPV) relies heavily on the new discovery of efficient OPV materials. In recent years, machine learning-assisted OPV material development has received wide attention, to overcome the inefficiency of traditional development mode. Herein, we proposed a new method that combines molecular fingerprints and regression models to achieve rapid prediction of the power conversion efficiency of newly-designed OPV donor materials. Based on the latest donor material database collected from the Web of Science database, the prediction accuracies of different combinations of molecular fingerprints and various machine learning regression models were compared systematically. We found that the combination of Morgan fingerprint and random forest model performs the best under the R-squared evaluation. And the combination of Hybridization fingerprint and support vector machine model performs the best under the mean absolute error evaluation. Moreover, a general trend is that the prediction accuracy of all models increases as the length of the molecular fingerprint increases. This method can be useful for preliminary screening of new OPV materials in a fast manner, and thus promotes the development of high-performance OPVs through accelerating the development of new OPV materials.

Key words： organic photovoltaics donor material power conversion efficiency prediction machine learning molecular fingerprint regression model

Published: 10 May 2021

CLC number:

TB303

Fund:National Natural Science Foundation of China (62074022, 12004057), the Natural Science Foundation of Chongqing (cstc2018jszx-cyzd0603), Venture & Innovation Support Program for Chongqing Overseas Returnees(cx2017034), Fundamental Research Funds for the Central Universities (2020CDJQY-A055), Key Laboratory of Low-grade Energy Utilization Technologies and Systems (LLEUTS-2020008).
†These authors contributed equally to this work.

About author:: Yujie Zheng is a lecturer at School of Energy and Power Engineering, Chongqing University. He received Bachelor's degree from Sichuan University and Ph.D. degree from National University of Singapore. He specializes in computational materials and machine lear-ning.
Xinbin Liang is an undergraduate student at School of Energy and Power Engineering, Chongqing University. He is interested in machine learning-assisted OPV material development.
Juan Du obtained Ph.D. degree in optics at Shanghai Institute of Optics and precision machinery of Chinese Academy of Sciences. She is a researcher and Ph.D. supervisor at Shanghai Institute of Optics and precision machinery, CAS. Her research directions include femtosecond laser technology and its application in the study of ultrafast molecular dynamics in chemical and biological systems.
Kuan Sun received his Ph.D. degree from the National University of Singapore in 2012. He joined Chongqing University as a “hundred talents” scholar in 2014. He has been engaged in the research on the principle and technology of efficient utilization of renewable energy.

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	ZHENG Yujie
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Cite this article:

ZHENG Yujie,LIANG Xinbin,ZHANG Qi, et al. Efficiency Prediction for Organic Photovoltaic Cells Using Molecular Fingerprints and Machine Learning Regression Models[J]. Materials Reports, 2021, 35(8): 8207-8212.

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http://www.mater-rep.com/EN/10.11896/cldb.20020113 OR http://www.mater-rep.com/EN/Y2021/V35/I8/8207

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