Perovskite solar cells (PSCs) represent a transformative photovoltaic technology, achieving a certified power conversion efficiency (PCE) of 27% and demonstrating the potential for low-cost manufacturing. However, their path to widespread commercialization is hindered by critical challenges in the operational stability and scalable fabrication of high-efficiency large-area modules. Traditional development cycles, reliant on trial-and-error, are too slow to address these complex, multi-faceted problems. In this context, machine learning (ML) emerges as a powerful fourth paradigm, capable of decoding non-linear structure-process-property relationships and accelerating the research and experimental development (R&D) pipeline. This review provides a unique synthesis of the PSCs' industrial landscape, analyzing the key bottlenecks of stability, scalability, and cost. It then establishes a novel framework that maps specific ML techniques from high-throughput virtual screening to process optimization as targeted solutions to these industrialization challenges. We detail how ML enables the rapid discovery of stable materials, predicts the device performance and lifetime, and optimizes manufacturing parameters, supported by emerging industrial case studies. Finally, we outline a strategic roadmap for the field, emphasizing the need for standardized data, explainable AI, and closed-loop automation to fully realize a data-driven future for PSC development and commercialization.
Pan et al. (Thu,) studied this question.