Enhancing perovskite solar cell efficiency and stability: a multimodal prediction approach integrating microstructure, composition, and processing technology
Wajeeha Rahman, Chengquan Zhong, Haotian Liu, Jingzi Zhang, Jiakai Liu, Kailong Hu, Xi Lin
Abstract
value of 0.95 (RMSE: 0.77) for bandgap estimation. Among the tested algorithms, the Gradient Boosting Regressor demonstrated superior performance. We also used machine learning to evaluate PSC stability, an essential factor for renewable energy applications. The model classified stability categories with AUC scores of 0.76 (moderately stable), 0.81 (very stable), and 0.78 (unstable), indicating robust performance with room for refinement. This research emphasizes the significant direct relationship between larger perovskite grain sizes and higher PCE, offering actionable insights for material optimization. The integrity of our experimental validation is supported by comprehensive testing across different device sizes and mass production verification, demonstrating the scalability of our framework. By integrating materials science and machine learning, this study advances the development of efficient, durable, and scalable PSCs, contributing to the broader adoption of renewable energy technologies.