Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials-Reference-Cited by-同舟云学术

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

Published:2019-11 Issue:11 Volume:5 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Sun Wenbo¹^ORCID,Zheng Yujie¹^ORCID,Yang Ke¹^ORCID,Zhang Qi¹^ORCID,Shah Akeel A.¹^ORCID,Wu Zhou²^ORCID,Sun Yuyang²^ORCID,Feng Liang³^ORCID,Chen Dongyang⁴^ORCID,Xiao Zeyun⁵^ORCID,Lu Shirong⁵,Li Yong⁶,Sun Kuan¹^ORCID

Affiliation:

1. 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.

2. MOE Key Laboratory of Dependable Service Computing in Cyber Physical Society, School of Automation, Chongqing University, Chongqing 400044, China.

3. College of Computer Science, Chongqing University, Chongqing 400044, China.

4. School of Electrical Engineering, North China University of Science and Technology, 21 Bohaidadao, Tangshan, Hebei 063210, China.

5. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fang Zheng Road, Beibei, Chongqing 400714, China.

6. College of Economics and Business Administration, Chongqing University, 174 Shazhengjie, Shapingba, Chongqing 400044, China.

Abstract

Machine learning can be a powerful tool to prescreen new materials, thus accelerating the development of the OPV field.

Funder

National Natural Science Foundation of China

Municipal Natural Science Foundation of Chongqing

National Youth Thousand Program Project

National Special Funds for Repairing and Purchasing Scientific Institutions

CAS Pioneer Hundred Talents Program B

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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