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, Wenbo1; Zheng, Yujie1; Yang, Ke1; Zhang, Qi1 ; Shah, Akeel A.1; Wu, Zhou2; Sun, Yuyang2; Feng, Liang3; Chen, Dongyang4; Xiao, Zeyun5
|
| 刊名 | Science Advances
 |
| 出版日期 | 2019 |
| 卷号 | 5期号:11 |
| ISSN号 | 2375-2548 |
| DOI | 10.1126/sciadv.aay4275 |
| 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. Copyright © 2019 The Authors, some rights reserved; |
| 电子版国际标准刊号 | 23752548 |
| 语种 | 英语 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9874]  |
| 专题 | 有机半导体材料研究中心 |
| 作者单位 | 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 |
| 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). |
| 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). |
| MLA | Sun, Wenbo,et al."Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials".Science Advances 5.11(2019). |
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。