Structural Disorder by Octahedral Tilting in Inorganic Halide Perovskites: New Insight with Bayesian Optimization

Abstract

Structural disorder is common in metal-halide perovskites and important for understanding the functional properties of these materials. First-principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure-sampling schemes required to characterize the disorder. Herein, structural disorder in the benchmark inorganic halide perovskites CsPbI3 and CsPbBr3 is computationally studied in terms of the three octahedral-tilting angles. The subsequent variations in energetics and properties are described by 3D potential-energy surfaces (PESs) and property landscapes, delivered by Bayesian optimization as implemented in the Bayesian optimization structure search code sampling density functional theory (DFT) calculations. The rapid convergence of the PES with about 200 DFT data points in 3D searches demonstrates the power of active learning and strategic sampling with Bayesian optimization. Further analysis indicates that disorder grows with increasing temperature and reveals that the material bandgap at finite temperatures is a statistical mean over disordered structures.Structural disorder phenomena of inorganic halide perovskites in terms of octahedral tilting around three lattice axes are computationally studied. Bayesian optimization machine learning technique assists to rapidly converge the three-dimensional potential energy surfaces. This study discovers that high-temperature perovskite phases are dynamic averages of disordered low-symmetry structures, and distinguishes the different roles of in-phase and out-of-phase tilts.image (c) 2024 WILEY-VCH GmbH