中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 重庆绿色智能技术研究院
  3. 中国科学院重庆绿色智能技术研究院
热门
Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

文献类型：期刊论文

作者Sun, Wenbo6; Zheng, Yujie6; Yang, Ke6; Zhang, Qi6; Shah, Akeel A.6; Wu, Zhou5; Sun, Yuyang5; Feng, Liang4; Chen, Dongyang3; Xiao, Zeyun2
刊名SCIENCE ADVANCES
出版日期2019-11-01
卷号5期号:11页码:8
ISSN号2375-2548
DOI10.1126/sciadv.aay4275
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.
资助项目National Youth Thousand Program Project[R52A199Z11] ; National Special Funds for Repairing and Purchasing Scientific Institutions[Y72Z090Q10] ; National Natural Science Foundation of China[21801238] ; CAS Pioneer Hundred Talents Program B[Y92A010Q10] ; Natural Science Foundation of Chongqing[cstc2018jcyjAX0556] ; Natural Science Foundation of Chongqing[cstc2017rgzn-zdyfX0030] ; Natural Science Foundation of Chongqing[cstc2017jcyjAX0451] ; Natural Science Foundation of Chongqing[cstc2017rgznzdyfX0023] ; Natural Science Foundation of Chongqing[cstc2018jszxcyzd0603]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000499736100095
出版者AMER ASSOC ADVANCEMENT SCIENCE
源URL[http://119.78.100.138/handle/2HOD01W0/10004]  
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, Coll Econ & Business Adm, 174 Shazhengjie, Chongqing 400044, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fang Zheng Rd, Chongqing 400714, Peoples R China
3.North China Univ Sci & Technol, Sch Elect Engn, 21 Bohaidadao, Tangshan 063210, Hebei, Peoples R China
4.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
5.Chongqing Univ, Sch Automat, MOE Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing 400044, Peoples R China
6.Chongqing Univ, Sch Energy & Power Engn, MOE Key Lab Low Grade Energy Utilizat Technol & S, 174 Shazhengjie, Chongqing 400044, Peoples R 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):8.
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),8.
MLA Sun, Wenbo,et al."Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials".SCIENCE ADVANCES 5.11(2019):8.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。