OSC-Net: a multi-fidelity machine learning model for organic solar cells
OSC-Net, a novel multi-fidelity machine learning framework, enables accurate prediction of organic solar cell (OSC) performance and accelerates OSC materials discovery.
Authors
- Haizhou Yang (ORCID: https://orcid.org/0000-0001-6408-5072)
- Junlin Ou (ORCID: https://orcid.org/0000-0003-2735-5840)
- Jeromy James Rech (ORCID: https://orcid.org/0000-0001-7963-9357)
- Yi Wang (ORCID: https://orcid.org/0000-0001-7001-9573)
- Wei You (ORCID: https://orcid.org/0000-0003-0354-1948)
- Adam Wold
Institutions
- University of North Carolina at Chapel Hill (US)
- University of South Carolina (US)
- Middle Tennessee State University (US)
- University of Michigan (US)
- University of North Carolina at Asheville (US)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-11-04
- DOI
- https://doi.org/10.1039/d5ta08724d
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Office of Naval Research