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

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Science Advances  08 Nov 2019:
Vol. 5, no. 11, eaay4275
DOI: 10.1126/sciadv.aay4275

Article Information

vol. 5 no. 11

Online ISSN: 
History: 
  • Received for publication June 18, 2019
  • Accepted for publication September 17, 2019
  • .

Author Information

  1. Wenbo Sun1,*,
  2. Yujie Zheng1,*,
  3. Ke Yang1,*,
  4. Qi Zhang1,
  5. Akeel A. Shah1,
  6. Zhou Wu2,
  7. Yuyang Sun2,
  8. Liang Feng3,
  9. Dongyang Chen4,
  10. Zeyun Xiao5,,
  11. Shirong Lu5,,
  12. Yong Li6 and
  13. Kuan Sun1,
  1. 1MOE 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. 2MOE Key Laboratory of Dependable Service Computing in Cyber Physical Society, School of Automation, Chongqing University, Chongqing 400044, China.
  3. 3College of Computer Science, Chongqing University, Chongqing 400044, China.
  4. 4School of Electrical Engineering, North China University of Science and Technology, 21 Bohaidadao, Tangshan, Hebei 063210, China.
  5. 5Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fang Zheng Road, Beibei, Chongqing 400714, China.
  6. 6College of Economics and Business Administration, Chongqing University, 174 Shazhengjie, Shapingba, Chongqing 400044, China.
  1. Corresponding author. Email: kuan.sun{at}cqu.edu.cn (K.S.); lushirong{at}cigit.ac.cn (S.L.); xiao.z{at}cigit.ac.cn (Z.X.)
  • * These authors contributed equally to this work.

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