Frendy Jaya Kusuma, Eri Widianto, Wahyono, Iman Santoso, Sholihun, Moh. Adhib Ulil Absor, Setyawan Purnomo Sakti, Kuwat Triyana
Perovskite solar cells (PSCs) have emerged as promising, cost-effective, and efficient alternatives to silicon-based solar cells, yet achieving both high stability and efficiency remains challenging. To address these challenges, we developed Random Forest and Extreme Gradient Boosting models to optimize the stability and power conversion efficiency (PCE) of PSCs, using a large dataset from the Perovskite Database. Our models demonstrated strong predictive performance, achieving an accuracy of 0.848 in stability classification and an R2 of 0.751 for PCE prediction on the test set. Stability prediction used a classification approach, labeling devices as stable if they retained at least 80 % of their initial PCE after 1,000 h, a threshold that allows the inclusion of both T80 and E1000h data. Using the trained models, we do high-throughput screening of 29,016 new device configurations with varied cell architectures, electron transport layers, hole transport layers, and perovskite ion compositions. Among these, we identified 100 top-performing, predicted stable lead-based PSCs configurations with potential PCEs reaching up to 26.06 %, surpassing the highest stable device in the Perovskite Database, which has a PCE of 22.3 %. This study demonstrates that machine learning-driven approaches can effectively guide PSCs optimization, surpassing the performance of previously reported configurations. © 2025 Elsevier Ltd
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, PO Box BLS 21, Yogyakarta, 55281, Indonesia; Department of Physics, Faculty of Engineering, Universitas Singaperbangsa Karawang, Telukjambe Timur, Karawang, 41361, Indonesia; Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara, PO Box BLS 21, Yogyakarta, 55281, Indonesia; Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Brawijaya, Jl. Veteran Malang, 65145, Indonesia
