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

材料导报

2021, Vol. 35

Issue (8): 8207-8212 https://doi.org/10.11896/cldb.20020113

高分子与聚合物基复合材料

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

郑玉杰^1,†, 梁鑫斌^1,†, 张起¹, 孙文博¹, 施童超^2,3, 杜鹃^2,3, 孙宽¹

1 重庆大学能源与动力工程学院,低品位能源利用技术及系统教育部重点实验室,重庆 400044
2 中国科学院上海光学精细机械研究所高场激光物理国家重点实验室,上海 201800
3 中国科学院大学材料科学与光电子工程中心,北京 100049

Efficiency Prediction for Organic Photovoltaic Cells Using Molecular Fingerprints and Machine Learning Regression Models

ZHENG Yujie^1,†, LIANG Xinbin^1,†, ZHANG Qi¹, SUN Wenbo¹, SHI Tongchao^2,3, DU Juan^2,3, SUN Kuan¹

1 MOE Key Laboratory of Low-grade Energy Utilization Technologies and Systems, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, China
2 State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
3 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

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摘要有机太阳能电池(OPV)的发展依赖于新型高效OPV材料的开发。近几年来,为解决传统有机太阳电池材料开发模式低效的问题,机器学习辅助OPV材料开发的新模式得到了广泛的关注。本工作提出一种结合分子指纹和机器学习回归模型的新方法,实现了OPV给体材料光电转换效率的快速预测。基于从文献中收集的给体材料数据库,系统地比较了不同分子指纹作为各种机器学习模型输入的预测精度。结果表明,Morgan分子指纹与随机森林模型的组合在决定系数指标下性能最优,而Hybridization分子指纹与支持向量机模型的组合在平均绝对误差指标下性能最优。同时,各模型的预测精度随着分子指纹的位数增加而提高。该方法可广泛用于新设计的OPV材料的快速初筛,从而提升新型OPV材料的研发速度,促进高性能OPV的快速发展。

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	郑玉杰
	梁鑫斌
	张起
	孙文博
	施童超
	杜鹃
	孙宽

关键词: 有机太阳能电池给体材料光电转换效率预测机器学习分子指纹回归模型

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

出版日期: 2021-04-25 发布日期: 2021-05-10

ZTFLH:

TB303

基金资助: 国家自然科学基金(62074022;12004057);重庆市自然科学基金(cstc2018jszx-cyzd0603);重庆市“留学人员创业创新支持计划”(cx2017034);中央高校基本科研业务费(2020CDJQY-A055);低品位能源利用技术及系统教育部重点实验室开放基金(LLEUTS-2020008)

通讯作者: kuan.sun@cqu.edu.cn;dujuan@mail.siom.ac.cn

作者简介: 郑玉杰,重庆大学能源与动力工程学院讲师。他先后获得四川大学学士学位和新加坡国立大学博士学位。研究专长为计算材料和机器学习。
梁鑫斌,本科生,于重庆大学能源与动力工程学院学习,主要从事机器学习方面的研究。
杜鹃,研究员,博士研究生导师。2004—2007年在中国科学院上海光学精密机械研究所攻读博士,获光学专业博士学位。2007—2010年在日本电气通信大学先进超快激光中心做博士后,并于2010年被提升为日本电气通信大学助理教授。2013年由上海光机所以“百人计划”引进,2014年5月获得中科院择优支持。主要研究方向为飞秒激光技术及其在化学、生物体系中超快分子动力学研究中的应用。
孙宽,重庆大学能源与动力工程学院副院长,低品位能源利用技术及系统教育部重点实验室副主任。2012年取得新加坡国立大学博士学位。随后在澳大利亚墨尔本大学、澳大利亚同步加速器、莫纳什大学及墨尔本纳米制备中心、德国卡尔斯鲁厄理工学院和马普所高分子研究所开展研究。2014年作为“百人计划”学者加入重庆大学。长期从事可再生能源高效利用原理及技术的研究。

引用本文:

郑玉杰, 梁鑫斌, 张起, 孙文博, 施童超, 杜鹃, 孙宽. 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[J]. 材料导报, 2021, 35(8): 8207-8212.
ZHENG Yujie, LIANG Xinbin, ZHANG Qi, SUN Wenbo, SHI Tongchao, DU Juan, SUN Kuan. Efficiency Prediction for Organic Photovoltaic Cells Using Molecular Fingerprints and Machine Learning Regression Models. Materials Reports, 2021, 35(8): 8207-8212.

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

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