High throughput molecular design of electron donors and non-fullerene acceptors using machine learning combined with substructure importance
In this study, we constructed a database to train a random forest model, identified key substructures through feature importance analysis, and designed and performed virtual screening for high-performance novel donor–acceptor molecules.
Authors
- Zi‐Jiang Liu (ORCID: https://orcid.org/0000-0002-0880-8149)
- Ji-Jun Gong (ORCID: https://orcid.org/0000-0002-0957-4831)
- Ming Li (ORCID: https://orcid.org/0000-0003-3580-2815)
- Xiaomeng Liu (ORCID: https://orcid.org/0009-0002-1656-0769)
- Hongshan Chen (ORCID: https://orcid.org/0000-0003-4811-4552)
- Youzhi Wu (ORCID: https://orcid.org/0000-0002-1508-1304)
- Cai‐Rong Zhang (ORCID: https://orcid.org/0000-0002-4067-2798)
- Long-Fei Lv
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/d5tc00931f
- Citations
- 4
- Primary Topic
- Organic Electronics and Photovoltaics
- Type
- article
- Field-Weighted Citation Impact
- 2.92
Funders
- National Natural Science Foundation of China