Machine Learning Approaches in Advancing Perovskite Solar Cells Research

Abstract The integration of machine learning (ML) with perovskite solar cells (PSCs) signifies a groundbreaking era in photovoltaic (PV) technology. The traditional iterative approaches in PSC research are often time‐consuming and resource‐intensive. In contrast, ML leverages available data and sophisticated algorithms to quickly identify properties and optimize parameters for novel materials and devices. This review explores how ML‐driven approaches are improving various facets of PSCs research, including the rapid screening of novel compositions, enhancing stability, refining device architectures, and deepening the understanding of underlying physics. The paper is structured to gradually familiarize readers with essential terminologies and concepts, ensuring a solid foundation before delving into more intricate topics. A concise workflow and various introductory toolkits for ML are also briefly discussed. Through a detailed analysis of compelling case studies, a basic research framework within ML‐PSC‐integrated research is provided. This comprehensive review can serve as a valuable reference for researchers aiming to understand and leverage ML‐driven approaches in PSCs research, advancing the path for more efficient and sustainable PV technologies.