中国科学院重庆绿色智能技术研究院机构知识库

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	Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
	Sun, Wenbo1; Zheng, Yujie1; Yang, Ke1; Zhang, Qi1; Shah, Akeel A.1; Wu, Zhou2; Sun, Yuyang2; Feng, Liang3; Chen, Dongyang4; Xiao, Zeyun5
	2019
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;
DOI	10.1126/sciadv.aay4275
发表期刊	Science Advances
ISSN	2375-2548
卷号	5期号:11
收录类别	SCI
语种	英语
EISSN	23752548