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Temperature-dependent anharmonic phonons in quantum paraelectric KTaO$_3$ by first principles and machine-learned force fields
arXiv - PHYS - Materials Science Pub Date : 2022-09-24 , DOI: arxiv-2209.12036
Luigi RanalliUniversity of Vienna, Faculty of Physics and Center for Computational Materials Science, Carla VerdiUniversity of Vienna, Faculty of Physics and Center for Computational Materials Science, Lorenzo MonacelliUniversity of Rome, Sapienza, Department of Physics, Matteo CalandraDepartment of Physics, University of Trento, Georg KresseUniversity of Vienna, Faculty of Physics and Center for Computational Materials Science, Cesare FranchiniUniversity of Vienna, Faculty of Physics and Center for Computational Materials ScienceAlma Mater Studiorum - University of Bologna, Department of Physics and Astronomy Augusto Righi

Understanding collective phenomena in quantum materials from first principles is a promising route toward engineering materials properties on demand and designing new functionalities. This work examines the quantum paraelectric state, an elusive state of matter characterized by the smooth saturation of the ferroelectric instability at low temperature due to quantum fluctuations associated with anharmonic phonon effects. The temperature-dependent evolution of the soft ferroelectric phonon mode in the quantum paraelectric KTaO$_3$ in the range 0-300 K is modelled by combining density functional theory (DFT) calculations with the stochastic self-consistent harmonic approximation assisted by an on-the-fly machine-learned force field. The calculated data show that including anharmonic terms is essential to stabilize the spurious imaginary ferroelectric phonon predicted by DFT, in agreement with experiments. Augmenting the DFT workflow with machine-learned force fields allows for efficient stochastic sampling of the configurational space using large supercells in a broad and dense temperature range, inaccessible by conventional ab initio protocols. This work proposes a robust computational workflow capable of accounting for collective behaviors involving different degrees of freedom and occurring at large time/length scales, paving the way for precise modeling and control of quantum effects in materials.

中文翻译:

基于第一原理和机器学习力场的量子顺电 KTaO$_3$ 中与温度相关的非谐声子

从第一原理理解量子材料中的集体现象是实现按需工程材料特性和设计新功能的有希望的途径。这项工作检查了量子顺电态,这是一种难以捉摸的物质状态,其特征是由于与非谐声子效应相关的量子涨落,铁电不稳定性在低温下会平滑饱和。软铁电声子模式在 0-300 K 范围内的量子顺电 KTaO$_3$ 中随温度变化的演化是通过将密度泛函理论 (DFT) 计算与随机自洽谐波逼近相结合来建模的。飞行机器学习力场。计算数据表明,与实验一致,包括非谐波项对于稳定由 DFT 预测的虚假假想铁电声子至关重要。使用机器学习的力场增强 DFT 工作流程,可以使用传统的从头算协议无法访问的宽而密集的温度范围内的大型超级单元对配置空间进行有效的随机采样。这项工作提出了一个强大的计算工作流程,能够解释涉及不同自由度并在大时间/长度尺度上发生的集体行为,为精确建模和控制材料中的量子效应铺平了道路。使用机器学习的力场增强 DFT 工作流程,可以使用传统的从头算协议无法访问的宽而密集的温度范围内的大型超级单元对配置空间进行有效的随机采样。这项工作提出了一个强大的计算工作流程,能够解释涉及不同自由度并在大时间/长度尺度上发生的集体行为,为精确建模和控制材料中的量子效应铺平了道路。使用机器学习的力场增强 DFT 工作流程,可以使用传统的从头算协议无法访问的宽而密集的温度范围内的大型超级单元对配置空间进行有效的随机采样。这项工作提出了一个强大的计算工作流程,能够解释涉及不同自由度并在大时间/长度尺度上发生的集体行为,为精确建模和控制材料中的量子效应铺平了道路。
更新日期:2022-09-27
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