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arXiv 2301.03497cond-mat.mtrl-sciphysics.comp-ph
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无机卤化物钙钛矿中机器学习势驱动的相变研究

Phase transitions in inorganic halide perovskites from machine learning potentials

Erik Fransson, Julia Wiktor, Paul Erhart

AI总结:

本研究利用机器学习势系统分析CsPbX3无机卤化物钙钛矿的相变,发现速率低于30 K/ns且尺寸达数万原子时收敛,并评估不同泛函对相变温度和晶格参数的预测精度,为相关模拟提供指导。

摘要:

卤化物钙钛矿的原子尺度动力学不仅直接影响其热稳定性,还影响其光电性质。机器学习势的进展直到最近才使得利用全原子方法以接近第一性原理的精度模拟这些材料的有限温度行为成为可能。在此,我们系统分析了加热和冷却速率、模拟尺寸、模型不确定性以及底层交换关联泛函对CsPbX3(X=Cl、Br、I)相行为的影响,涵盖钙钛矿相和δ相。我们表明,速率低于约30 K/ns且系统尺寸至少为几万个原子时,才能在这些参数方面实现收敛。通过控制这些因素并构建针对不同交换关联泛函的特定模型,我们进一步表明,本工作所考虑的半局域泛函(SCAN、vdW-DF-cx、PBEsol和PBE)系统地低估了分离钙钛矿相的转变温度,同时高估了晶格参数。在所考虑的泛函中,vdW-DF-cx泛函与实验的吻合度最高,其次是SCAN、PBEsol和PBE。我们的工作为无机卤化物钙钛矿及类似系统中动力学和相变的系统分析提供了指导方针,同时也为机器学习势以及交换关联泛函的进一步发展提供了基准。

英文摘要:

The atomic scale dynamics of halide perovskites have a direct impact not only on their thermal stability but their optoelectronic properties. Progress in machine learned potentials has only recently enabled modeling the finite temperature behavior of these material using fully atomistic methods with near first-principles accuracy. Here, we systematically analyze the impact of heating and cooling rate, simulation size, model uncertainty, and the role of the underlying exchange-correlation functional on the phase behavior of CsPbX3 with X=Cl, Br, and I, including both the perovskite and the delta-phases. We show that rates below approximately 30 K/ns and system sizes of at least a few ten thousand atoms are indicated to achieve convergence with regard to these parameters. By controlling these factors and constructing models that are specific for different exchange-correlation functionals we then show that the semi-local functionals considered in this work (SCAN, vdW-DF-cx, PBEsol, and PBE) systematically underestimate the transition temperatures separating the perovskite phases while overestimating the lattice parameters. Among the considered functionals the vdW-DF-cx functional yields the closest agreement with experiment, followed by SCAN, PBEsol, and PBE. Our work provides guidelines for the systematic analysis of dynamics and phase transitions in inorganic halide perovskites and similar systems. It also serves as a benchmark for the further development of machine-learned potentials as well as exchange-correlation functionals.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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