Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.

Inorganic and hybrid lead halide perovskites are among the most promising candidates for next-generation optoelectronic devices. However, their further development is critically constrained by stability issues, including phase degradation, migration, and interfacial reactions. Overcoming these challenges requires a detailed understanding of the underlying atomic-scale mechanisms. Density functional theory (DFT) and ab initio molecular dynamics (AIMD) offer essential electronic and structural insights, but their high computational cost limits simulations to relatively small systems and short time scales, making it difficult to capture long-term evolution. Classical MD can access larger length and time scales but often lacks accurate and transferable force fields for lead halide perovskite systems. Machine learning potentials (MLPs) have emerged as an alternative solution to bridge this gap, enabling large-scale, long-time-scale MD simulations with near-DFT accuracy at a fraction of the computational cost. In this perspective, we review recent applications of MLPs to inorganic and hybrid lead halide perovskites, including phase behavior, ion migration, and perovskite interfaces. We further discuss current challenges related to model efficiency and transferability and outline future opportunities for deploying MLPs to tackle outstanding questions in perovskite stability, degradation, and device-relevant operation.