Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

Data‐driven epoch, the development of machine learning (ML) in materials and device design is an irreversible trend. Its ability and efficiency to handle nonlinear and game‐playing problems is unmatched by traditional simulation computing software and trial‐error experiments. Perovskite solar cells are complex physicochemical devices (systems) that consist of perovskite materials, transport layer materials, and electrodes. Predicting the physicochemical properties and screening the component materials related to perovskite solar cells is the strong point of ML. However, the applications of ML in perovskite solar cells and component materials has only begun to boom in the last two years, so it is necessary to provide a review of the involved ML technologies, the application status, the facing urgent challenges and the development blueprint. The 5 key technologies (Data sources and regularization; Feature extraction and transformation of molecular descriptors; Selection of suitability algorithms; Model validation and optimization; Model interpretation), applications, milestone research, urgent challenges and future application directions of machine learning in the field of perovskite solar cells, and component materials are reviewed.