Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review

With the global increase in energy demand and environmental awareness, it has become crucial to develop new types of energy materials that are efficient, stable and environmentally friendly. Lead-free perovskite materials have garnered attention due to their unique crystal structure (ABX 3 ) and photoelectric properties, particularly demonstrating great potential for applications such as photovoltaics, photodetectors, catalysis, and display lighting. However, the lead toxicity of traditional lead-containing perovskite materials limits their large-scale commercialization. Therefore, the research on stable and non-toxic lead-free perovskite materials has become a current hot topic in scientific research. In recent years, artificial intelligence technology has brought about a transformation in the study of perovskite materials. This review focuses on the application of AI in lead-free perovskite research, including data collection, preprocessing, feature extraction, model training and prediction, reverse design and experimental verification. This paper aims to leverage AI technologies to drive data-informed and inverse-designed discovery processes, thereby improving the efficiency and success rate of lead-free perovskite materials screening, development, and performance optimization.