Machine Learning Unlocks New Directions in Halide Perovskite Research

Halide perovskites have attracted significant interest due to their potential in optoelectronic devices. However, challenges related to complex compositional spaces, environmental sensitivity, and stability limitations continue to constrain their systematic development and application. Machine learning (ML) has emerged as an effective tool to address these challenges by enabling the prediction of material properties, the identification of promising compositions, and optimization of processing conditions, while reducing reliance on conventional trial‐and‐error methods. By capturing complex, nonlinear relationships among compositional, structural, and processing parameters, ML enables the exploration of broad design spaces that are essential for advancing perovskite research. Additionally, ML accelerates the discovery and optimization of perovskite materials through data‐driven approaches, including high‐throughput screening and inverse design, enabling rapid identification of optimal compositions and processing conditions for enhanced device performance and stability. This review provides an overview of recent efforts to integrate ML into halide perovskite studies, discussing workflows, implementation strategies, and notable progress in device‐level development. This article highlights how ML enables systematic materials discovery and optimization, supporting the advancement of stable and efficient perovskite optoelectronic devices.