Machine Learning for Stability Enhancement in Perovskite Solar Cells: A Pathway to Commercial Viability

ABSTRACT Organic–inorganic hybrid perovskites hold significant promise for low‐cost, high‐efficiency, and scalable photovoltaic production. However, stability challenges of perovskite materials hinder their commercial viability. Although significant progress in enhancing stability has been achieved through compositional adjustments, additive engineering, and solvent‐based processing strategies, these methods often involve laborious and time‐consuming optimization processes. Machine learning (ML) is proving highly effective for accelerating the development and optimization of stable perovskite materials, reducing reliance on trial‐and‐error methods. This review outlines the fundamental ML workflow and highlights its applications in material screening, mechanism investigation, and characterization analysis in perovskite solar cells (PSCs) research. These key advancements underscore the utility of ML in systematically improving the durability of PSCs. Future integration of ML with high‐throughput experimentation is expected to further advance the development of efficient, stable, and commercially viable PSCs, contributing to sustainable energy solutions.