Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

doi:10.1126/sciadv.aay4275

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 有机半导体材料研究中心

	Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
	Sun, Wenbo 2; Zheng, Yujie 2; Yang, Ke 2; Zhang, Qi 2; Shah, Akeel A.2; Wu, Zhou 3; Sun, Yuyang 3; Feng, Liang 1; Chen, Dongyang 6; Xiao, Zeyun 5
刊名	Science Advances
	2019
卷号	5 期号:11
ISSN号	2375-2548
DOI	10.1126/sciadv.aay4275
英文摘要	In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field. Copyright © 2019 The Authors, some rights reserved;
电子版国际标准刊号	23752548
语种	英语
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/9874]
专题	有机半导体材料研究中心
作者单位	1.College of Computer Science, Chongqing University, Chongqing; 400044, China; 2.MOE Key Laboratory of Low-grade Energy Utilization Technologies and Systems, School of Energy and Power Engineering, Chongqing University, 174 Shazhengjie, Shapingba; Chongqing; 400044, China; 3.MOE Key Laboratory of Dependable Service Computing in Cyber Physical Society, School of Automation, Chongqing University, Chongqing; 400044, China; 4.College of Economics and Business Administration, Chongqing University, 174 Shazhengjie, Shapingba; Chongqing; 400044, China 5.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fang Zheng Road, Beibei, Chongqing; 400714, China; 6.School of Electrical Engineering, North China University of Science and Technology, 21 Bohaidadao, Tangshan, Hebei; 063210, China;
推荐引用方式 GB/T 7714	Sun, Wenbo,Zheng, Yujie,Yang, Ke,et al. Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials[J]. Science Advances,2019,5(11).
APA	Sun, Wenbo.,Zheng, Yujie.,Yang, Ke.,Zhang, Qi.,Shah, Akeel A..,...&Sun, Kuan.(2019).Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials.Science Advances,5(11).
MLA	Sun, Wenbo,et al."Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials".Science Advances 5.11(2019).