Journal article
The Use of Deep Learning to Fast Evaluate Organic Photovoltaic Materials
Wenbo Sun, Meng Li, Yong Li, Zhou Wu, Yuyang Sun, Shirong Lu, Zeyun Xiao, Baomin Zhao, Kuan Sun
Advanced Theory and Simulations | Wiley-Blackwell | Published : 2019
Abstract
It is acknowledged that the structure of a material determines its activity or property. During the development of organic photovoltaic (OPV) materials, it is vitally important to build the relationship between chemical structures and photovoltaic properties. However, the conventional way based on trial‐and‐error experiments requires a significant amount of time and resources. Here, it is demonstrated that deep learning can be employed to quickly evaluate the performance of new OPV materials. The deep learning model allows direct use of pictures of chemical structures as input, possesses an excellent nonlinear analyzing capability, and has a low demand for computing power. After training the..
View full abstractGrants
Awarded by Natural Science Foundation of China
Awarded by Natural Science Foundation of Chongqing
Awarded by Key Laboratory of Low-grade Energy Utilization Technologies and Systems
Awarded by Venture & Innovation Support Program for Chongqing Overseas Returnees
Awarded by Fundamental Research Funds for the Central Universities
Funding Acknowledgements
The authors thank the Harvard Clean Energy Project for building the database for this study. The authors also thank Kaili Wang, Wei Meng, Chen Wang, Wei Chen, Falin Wu, Jiehao Fu, Rui Chen, Ke Yang, Lijun Hu, Zhuang Xiong, and Jielei Xi for their help in consolidating the raw data. This research was supported by research grants from the Natural Science Foundation of China (61504015, 51702032), the Natural Science Foundation of Chongqing (cstc2017jcyjA0752), the Key Laboratory of Low-grade Energy Utilization Technologies and Systems (LLEUTS-2017004), Venture & Innovation Support Program for Chongqing Overseas Returnees (cx2017056, cx2017034), and Fundamental Research Funds for the Central Universities (106112017CDJQJ148805).