Mass-customization of organic photovoltaics and data production for machine learning models precisely predicting device behavior
PP and J – V prediction models were developed using the PM6:L8-BO dataset. By combining an accumulated and a small new-component dataset, ML model predicts ternary device performance enabling generalization of the model to unexplored material systems.
Authors
- Zhixiang Wei (ORCID: https://orcid.org/0000-0001-6188-3634)
- Seyeong Song (ORCID: https://orcid.org/0000-0003-4479-145X)
- Yinhua Zhou (ORCID: https://orcid.org/0000-0001-6424-9962)
- Na An (ORCID: https://orcid.org/0000-0003-3099-2736)
- Doojin Vak (ORCID: https://orcid.org/0000-0001-7704-5563)
- Leonard W. T. Ng (ORCID: https://orcid.org/0000-0001-6062-7823)
- Udo Bach (ORCID: https://orcid.org/0000-0003-2922-4959)
- Yang Liu (ORCID: https://orcid.org/0000-0001-9704-8120)
- Jin Young Kim (ORCID: https://orcid.org/0000-0002-6595-4468)
- Mei Gao (ORCID: https://orcid.org/0000-0002-1781-6132)
- Chang‐Qi Ma (ORCID: https://orcid.org/0000-0002-9293-5027)
Institutions
- Nanyang Technological University (SG)
- Gyeongsang National University (KR)
- Chinese Academy of Sciences (CN)
- Suzhou Institute of Nano-tech and Nano-bionics (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- National Center for Nanoscience and Technology (CN)
- CSIRO Manufacturing (AU)
- Huazhong University of Science and Technology (CN)
- Ulsan National Institute of Science and Technology (KR)
- Monash University (AU)
Publication Details
- Journal
- Energy & Environmental Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ee02815a
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.55