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Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials

文献类型：期刊论文

作者Sun, Wenbo1,7; Zheng, Yujie1; Zhang, Qi1; Yang, Ke1,6; Chen, Haiyan6; Cho, Yongjoon5; Fu, Jiehao6; Odunmbaku, Omololu1; Shah, Akeel A.1; Xiao, Zeyun6
刊名JOURNAL OF PHYSICAL CHEMISTRY LETTERS
出版日期2021-09-16
卷号12期号:36页码:8847-8854
ISSN号1948-7185
DOI10.1021/acs.jpclett.1c02554
通讯作者Xiao, Zeyun(xiao.z@cigit.ac.cn) ; Lu, Shirong(lushirong@cigit.ac.cn) ; Sun, Kuan(kuan.sun@cqu.edu.cn)
英文摘要Designing efficient organic photovoltaic (OPV) materials purposefully is still challenging and time-consuming. It is of paramount importance in material development to identify basic functional units that play the key roles in material performance and subsequently establish the substructure-property relationship. Herein, we describe an automatic design framework based on an in-house designed La FREMD Fingerprint and machine learning (ML) algorithms for highly efficient OPV donor molecules. The key building blocks are identified, and a library consisting of 18 960 new molecules is generated within this framework. Through investigating the chemical structures of materials with different performance, a guidance on designing efficient OPV materials is proposed. Furthermore, the most promising candidates exhibit a predicted power conversion efficiency (PCE) value of over 15% when combined with acceptor Y6. Density functional theory (DFT) studies show these candidate materials possess exceptional potential for efficient charge carrier transport. The proposed framework demonstrates the ability to design new materials based on the substructure-property relationship built by ML, which provides an alternative methodology for applying ML in new material discovery.
资助项目China Scholarship Council ; Natural Science Foundation of China[62074022] ; Natural Science Foundation of China[12004057] ; Natural Science Foundation of China[22071238] ; Natural Science Foundation of China[62074149] ; Fundamental Research Funds for the Central Universities[2020CDJQY-A055] ; Key Laboratory of Low-Grade Energy Utilization Technologies and Systems[LLEUTS2019001]
WOS研究方向Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
出版者AMER CHEMICAL SOC
WOS记录号WOS:000697334300020
源URL[http://119.78.100.138/handle/2HOD01W0/14341]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Xiao, Zeyun; Lu, Shirong; Sun, Kuan
作者单位1.Chongqing Univ, Sch Energy & Power Engn, MOE Key Lab Low Grade Energy Utilizat Technol & S, Chongqing 400044, Peoples R China
2.Computat Sci Appl Res CSAR Inst Shenzhen, Shenzhen 518110, Peoples R China
3.Computat Sci Res Ctr CSRC Beijing, Shenzhen 518110, Peoples R China
4.Chongqing Univ, Coll Chem & Chem Engn, Chongqing 400044, Peoples R China
5.Ulsan Natl Inst Sci & Technol UNIST, Low Dimens Carbon Mat Ctr, Sch Energy & Chem Engn, Perovtron Res Ctr,Dept Energy Engn, Ulsan 44919, South Korea
6.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
7.Univ Bremen, Bremen Ctr Computat Mat Sci, D-28359 Bremen, Germany
推荐引用方式
GB/T 7714		 Sun, Wenbo,Zheng, Yujie,Zhang, Qi,et al. Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials[J]. JOURNAL OF PHYSICAL CHEMISTRY LETTERS,2021,12(36):8847-8854.
APA Sun, Wenbo.,Zheng, Yujie.,Zhang, Qi.,Yang, Ke.,Chen, Haiyan.,...&Sun, Kuan.(2021).Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials.JOURNAL OF PHYSICAL CHEMISTRY LETTERS,12(36),8847-8854.
MLA Sun, Wenbo,et al."Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials".JOURNAL OF PHYSICAL CHEMISTRY LETTERS 12.36(2021):8847-8854.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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