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

Jingrui Li, Fang Pan, Guo-Xu Zhang, Zenghui Liu, Hua Dong, Dawei Wang, Zhuangde Jiang, Wei Ren, Zuo-Guang Ye, Milica Todorović, Patrick Rinke
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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.

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无机卤化物包光体中八面体倾斜造成的结构紊乱:贝叶斯优化的新见解
结构无序是金属卤化物包晶体中常见的现象,对于了解这些材料的功能特性非常重要。第一原理方法可以解决原子尺度上的结构变化问题,但由于缺乏表征无序所需的结构取样方案,这些方法往往受到限制。在此,我们根据三个八面体倾角对基准无机卤化物包晶石 CsPbI3 和 CsPbBr3 的结构无序性进行了计算研究。随后的能量和性质变化由三维势能面(PES)和性质景观来描述,并通过贝叶斯优化结构搜索代码采样密度泛函理论(DFT)计算来实现。三维搜索中约 200 个 DFT 数据点的 PES 快速收敛表明了贝叶斯优化的主动学习和策略采样的威力。进一步的分析表明,无序度会随着温度的升高而增加,并揭示了有限温度下的材料带隙是无序结构的统计平均值。
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