Unified Graph-Based Interatomic Potential for Perovskite Structure Optimization

Halide perovskites (HaPs) hold immense potential for applications such as optoelectronics and catalysis. Their vast compositional space, spanning bulk alloys, defects, impurities, surfaces, and surface defects, poses significant challenges for efficient exploration and optimization. To address this, we trained a unified graph-based deep learned interatomic potential capable of optimizing and predicting energetics across these diverse structural motifs and navigating the complex potential energy surface (PES). Using a comprehensive density functional theory data set of HaP structures, which includes bulk alloys, native and impurity defects, and surface slabs, we rigorously trained and benchmarked the M3GNet-based machine learning interatomic potential (IAP). The M3GNet-IAP framework, trained on DFT-calculated energies, forces, and stresses, enables gradient-based optimization and efficient exploration of the PES. Our models, trained on a data set of ∼12,000 HaP structures across diverse structural domains, demonstrated robust generalizability across the complex PES, achieving low errors (energies ( E ): 3.7 meV/atom; forces ( f ): 16.5 meV/Å; stresses ( σ ): 5.5 MPa), and accurately predicting formation energies, decomposition energies, defect energies, and surface energies. Our unified surrogate model provides a holistic approach to geometry optimization across different structural variations in HaPs and will be transformative for the discovery of promising new compositions, important defects and dopants, and surface properties.