Machine Learning-Guided Discovery of High-Performance Perovskite Solar Cells via Cluster Analysis and Experimental Validation
, which exhibited a champion efficiency of 22.06%, aligning closely with the predicted performance. Notably, the proposed method reduces the experimental search space by 3 orders of magnitude. Feature importance analysis confirmed that composition (formamidinium, methylammonium, cesium) is the primary performance driver, experimentally validating our clustering method. This work provides a generalizable tool and a significant roadmap for the data-driven design of complex functional materials, effectively bridging the gap between computational discovery and experimental success.
Authors
- Riffat Jehan
- Xu Zhu (ORCID: https://orcid.org/0000-0001-8624-2905)
- Chengquan Zhong (ORCID: https://orcid.org/0000-0001-7409-3367)
- Wajeeha Rahman (ORCID: https://orcid.org/0000-0001-6638-7760)
- Shahid Hameed Ullah
- Jingzi Zhang
- Jiakai Liu
- Kailong Hu
Institutions
- Jimei University (CN)
- Chinese Academy of Sciences (CN)
- Harbin Institute of Technology (CN)
- Xinjiang Technical Institute of Physics & Chemistry (CN)
- Centre for Sustainable Energy (GB)
- University of Chinese Academy of Sciences (CN)
- Xiamen Tobacco Industry (China) (CN)
- College of Marin (US)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2026-03-31
- DOI
- https://doi.org/10.1021/acsami.5c25501
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Science and Technology Foundation of Shenzhen City
- China Postdoctoral Science Foundation
- Jimei University
- Basic and Applied Basic Research Foundation of Guangdong Province