The Use of Deep Learning to Fast Evaluate Organic Photovoltaic Materials-Reference-Cited by-同舟云学术

The Use of Deep Learning to Fast Evaluate Organic Photovoltaic Materials

Published:2018-11-13 Issue:1 Volume:2 Page:1800116
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Adv. Theory Simul.

Author:

Sun Wenbo¹,Li Meng¹,Li Yong¹²,Wu Zhou³,Sun Yuyang³,Lu Shirong⁴,Xiao Zeyun⁴,Zhao Baomin⁵,Sun Kuan¹^ORCID

Affiliation:

1. MOE Key Laboratory of Low‐grade Energy Utilization Technologies and SystemsSchool of Energy and Power EngineeringChongqing University 174 Shazhengjie Shapingba, Chongqing 400044 China

2. School of Economics and Business AdministrationChongqing University 174 Shazhengjie Shapingba, Chongqing 400044 China

3. MOE Key Laboratory of Dependable Service Computing in Cyber Physical SocietySchool of AutomationChongqing University 174 Shazhengjie Shapingba, Chongqing 400044 China

4. Chongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences 266 Fang Zheng Road Beibei, Chongqing 400714 China

5. Key Laboratory for Organic Electronics and Information DisplaysInstitute of Advanced MaterialsNanjing University of Posts and Telecommunications 66 New Model Road Nanjing 210023 China

Funder

Natural Science Foundation of Chongqing

National Natural Science Foundation of China

Publisher

Wiley

Subject

Multidisciplinary,Modeling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.201800116

Reference68 articles.

1. A polymer tandem solar cell with 10.6% power conversion efficiency

2. A molecular nematic liquid crystalline material for high-performance organic photovoltaics