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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
2019-11-01
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.
DOI10.1126/sciadv.aay4275
发表期刊SCIENCE ADVANCES
ISSN2375-2548
卷号5期号:11页码:8
通讯作者Xiao, Zeyun(xiao.z@cigit.ac.cn) ; Lu, Shirong(lushirong@cigit.ac.cn) ; Sun, Kuan(kuan.sun@cqu.edu.cn)
收录类别SCI
WOS记录号WOS:000499736100095
语种英语