Molecular design of organic photovoltaic donors and non-fullerene acceptors: a combined machine learning and genetic algorithm approach
A 480-pair donor–acceptor database with 43 descriptors predicted photovoltaic parameters via random forest. Novel pairs via genetic algorithm achieved 16.85% efficiency, offering an efficient method for organic solar cells and molecular design.
Authors
- Zi‐Jiang Liu (ORCID: https://orcid.org/0000-0002-0880-8149)
- Ji-Jun Gong (ORCID: https://orcid.org/0000-0002-0957-4831)
- Meiling Zhang (ORCID: https://orcid.org/0000-0003-2388-6716)
- Rui Cao (ORCID: https://orcid.org/0000-0001-5506-4464)
- Xiaomeng Liu (ORCID: https://orcid.org/0000-0002-3288-212X)
- Hongshan Chen (ORCID: https://orcid.org/0000-0002-9365-8789)
- Youzhi Wu (ORCID: https://orcid.org/0000-0002-1508-1304)
- Cai‐Rong Zhang (ORCID: https://orcid.org/0000-0002-4067-2798)
Institutions
- Lanzhou University of Technology (CN)
- Lanzhou Jiaotong University (CN)
- Northwest Normal University (CN)
Publication Details
- Journal
- Journal of Materials Chemistry C
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5tc02014j
- Citations
- 7
- Primary Topic
- Organic Electronics and Photovoltaics
- Type
- article
- Field-Weighted Citation Impact
- 5.11
Funders
- National Natural Science Foundation of China