Unveiling Hidden Correlations Between Fabrication Parameters and Resulting Outcomes in Tin‐Based Perovskite Solar Cells via Machine Learning

Tin‐based perovskite solar cells (TPSCs) have emerged as an alternative to traditional lead‐based perovskite solar cells (PSCs) due to their eco‐friendliness and optoelectronic properties. However, their limited long‐term stability and lower power conversion efficiency (PCE) arise from rapid oxidation and uncontrolled crystallization. The performance of these TPSCs is highly influenced by complex interactions between fabrication parameters and the resulting morphological, electrical and optical properties. Conventional experimental methods are time‐consuming and fail to capture the nonlinear relationships. This study builds a machine learning (ML) framework to correlate process parameters, such as annealing protocols, precursor materials, additive materials and other parameters, with resulting outcomes, thereby providing optimisation parameters for highly stable TPSCs. A data‐driven, combined ML framework was employed, employing both unsupervised and supervised learning. To improve data quality, various processing techniques were used, including iterative imputation, a standard scaler and a one‐hot encoder. A random forest ML model was optimised using Grid search CV to improve its performance. The optimised random forest model achieved a coefficient of determination ( R 2 ) of 0.93 (train) and 0.88 (test), demonstrating robust predictive performance. SHapley Additive exPlanations (SHAP) analyses highlighted diethylammonium chloride (DEACI) and phenethylammonium bromide (PeABr) as influential materials, which help reduce predicted trap densities and improve the overall performance of the trained model. This study provides a data‐driven analytical framework for optimising fabrication parameters that reduces the trial‐and‐error methods. These identified fabrication parameters and material compositions provide guidance for optimisation strategies in future experimental studies and TPSC fabrication.