Structural disorder by octahedral tilting in inorganic halide perovskites: New insight with Bayesian optimization

TL;DR

This study uses Bayesian optimization combined with DFT calculations to efficiently explore structural disorder in inorganic halide perovskites, revealing how octahedral tilting influences energetics and band gaps at finite temperatures.

Contribution

It introduces a Bayesian optimization approach to systematically sample and analyze structural disorder in perovskites, enhancing understanding of their temperature-dependent properties.

Findings

Disorder increases with temperature.

Band gap at finite temperature is a statistical average.

Rapid convergence of potential-energy surfaces with ~200 DFT points.

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. In this work, structural disorder in the benchmark inorganic halide perovskites CsPbI$_3^{}$ and CsPbBr$_3^{}$ is computationally studied in terms of the three octahedral-tilting angles. The consequent variation in energetics and properties are described by three-dimensional potential-energy surfaces (PESs) and property landscapes, delivered by Bayesian Optimization Structure Search method with integrated density-functional-theory (DFT) calculations. The rapid convergence of the PES with about 200 DFT data points in three-dimensional 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 materials band gap at finite temperatures is a statistical mean over disordered structures.