Machine-learning-assisted exploration of new non-fullerene acceptors for high-efficiency organic solar cells
Authors
- Yue Zang (ORCID: https://orcid.org/0000-0003-1053-5263)
- Wensheng Yan (ORCID: https://orcid.org/0000-0002-6364-3471)
- Tianxiang Shao (ORCID: https://orcid.org/0000-0003-4593-7756)
- Yibo Tu
- Zhikang Zhu (ORCID: https://orcid.org/0000-0003-3541-6420)
- Yida Wang (ORCID: https://orcid.org/0000-0003-4519-9108)
- Yiming Liu (ORCID: https://orcid.org/0000-0003-3352-4268)
- Chenyang Zhu (ORCID: https://orcid.org/0009-0008-3218-1543)
- Weihong Liu (ORCID: https://orcid.org/0000-0002-1102-6156)
- Qingya Wei (ORCID: https://orcid.org/0000-0001-9479-4566)
Institutions
- China Three Gorges University (CN)
- Hangzhou Dianzi University (CN)
Publication Details
- Journal
- Cell Reports Physical Science
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1016/j.xcrp.2024.102316
- Citations
- 8
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Zhejiang Province