Machine Learning Descriptors for Mapping Structure-Property-Performance Relationships of Perovskite Solar Cells

In recent years, machine learning (ML) has emerged as a versatile tool for accelerating the development of perovskite solar cells (PSCs). A key challenge, however, lies in the scarcity of researchers possessing deep expertise in both material science and artificial intelligence. Pivotal to bridging this gap is ML descriptors, mediating between the empirical language of materials and the numerical inputs for ML algorithms. By translating domain knowledge into computationally tractable forms, the descriptors significantly enhance the model interpretability and empower researchers to uncover the underlying physical mechanisms governing behavior of PSCs. Therefore, it is crucial to overview the efforts translating the structure, property of perovskite materials and performance of PSCs into numerical descriptors compatible with ML models. This review summarized (1) the encoding of crystal structure in perovskites; (2) the quantification of microstructures in perovskite films; (3) the stability assessment of perovskite materials and devices. By synthesizing progress in these aspects, this work lays a solid foundation for constructing a universal model to elucidate the structure-property-performance relationships in PSCs, especially in forward prediction and backward inference.