Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives

Abstract

Within a short period of time, perovskite solar cells (PSC) have attracted paramount research interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC research is significantly accelerated their holistic understanding of device requisite properties. ML techniques are increasingly employed to discover stable perovskite materials, optimize device architecture and processing, and analyze PSC characterization data. This review provides an in‐depth exploration of ML applications in PSC advancement through an analysis of existing literature. The review commences with an introduction to the ML workflow, detailing each step, followed by concise overviews of perovskite materials and PSC operation. Later sections explore the diverse ways ML contributes to PSC development, which ranges from the optoelectronic property prediction of perovskites, discovery of novel perovskites, PSC device structure optimization, and comprehensive PSC analyses. The challenges impeding PSC commercialization are discussed, along with ML's potential to mitigate them. The review concludes by highlighting current limitations in employing ML for PSC research and suggests potential solutions. It also outlines prospective research directions for ML applications in PSC research, aiming to develop highly efficient and stable PSCs.