Deep-learning enabled atomic insights into the phase transitions and nanodomain topology of lead-free(K,Na)NbO_(3) ferroelectrics  

深度学习分子动力学模拟助力无铅(K,Na)NbO_(3)铁电材料相变及纳米畴结构研究

作　　者：Xu Zhang Bei Li Ji Zou Hanxing Liu Ben Xu Kai Liu 张旭;李蓓;邹冀;刘韩星;徐贲;刘凯(School of Materials Science and Engineering,Research Center for Materials Genome Engineering,Wuhan University of Technology,Wuhan,430070,China;State Key Laboratory of Advanced Technology for Materials Synthesis and Processing,Wuhan University of Technology,Wuhan,430070,China;International School of Materials Science and Engineering,Wuhan University of Technology,Wuhan,430070,China;Graduate School,China Academy of Engineering Physics,Beijing,100193,China)

机构地区：[1]School of Materials Science and Engineering,Research Center for Materials Genome Engineering,Wuhan University of Technology,Wuhan,430070,China [2]State Key Laboratory of Advanced Technology for Materials Synthesis and Processing,Wuhan University of Technology,Wuhan,430070,China [3]International School of Materials Science and Engineering,Wuhan University of Technology,Wuhan,430070,China [4]Graduate School,China Academy of Engineering Physics,Beijing,100193,China

出　　处：《Science China Materials》2024年第9期3029-3038,共10页中国科学（材料科学）（英文版）

基　　金：supported by the National Key Research and Development Program of China(2021YFB3703100 and 2023YFB3812200);the National Natural Science Foundation of China(52202066);the Joint Fund of Ministry of Education for Preresearch of Equipment(8091B032105);the Fundamental Research Funds for the Central Universities(2020-YB-008)。

摘　　要：Lead-free K_(x)Na_(1-x)NbO_(3)(KNN)perovskites have garnered increasing attention due to their exceptional ferropiezoelectric properties,which are effectively tuned via polymorphic structures and domain dynamics.However,atomic insights into the underlying nanomechanisms governing the ferroelectricity of KNNs amidst varying factors such as composition,phase,and domain are still imperative.Here,we perform molecular dynamics simulations of phase transitions and domain dynamics for KNNs with various K/Na ratios(x=0.25∼1.0)by using ab-initio accuracy deep learning potential(DP).As a demonstration of its transferability,the newly developed DP model shows quantum accuracy in terms of the equation of states,elastic constants,and phonon dispersion relations for various KNbO_(3)and K_(0.5)Na_(0.5)NbO_(3).Furthermore,intricate temperature-dependent phase transitions and domain formation of KNNs are extensively and quantitatively captured.Simulations indicate that for KNNs with compositions x ranging from 0.25 to 1.0,the paraelectric-to-ferroelectric phase transition of KNNs is driven primarily by the order-disorder effect,while the displacive effect is dominant in the subsequent ferroelectric phase transitions.Specifically,flux-closure or herringbone-like nanodomain patterns arranged with 90°domain walls formed close to the experimental observations.Detailed analyses reveal that favorable 90°domain wall formation becomes more challenging with increasing Na content due to distinct oxygen octahedron distortion arising from the different ionic radii of K/Na atoms.It is envisioned that the combination of unified DP and atomistic simulations will help offer a robust solution for more accurate and efficient in silico explorations of complex structural,thermodynamic,and ferroelectric properties for relevant energy storage and conversion materials.通过多态结构和畴动力学调控,无铅K_(x)Na_(1-x)NbO_(3)(KNN)可拥有优异的铁电和压电性能,并因此获得持续关注.尽管通过组分、相和畴结构等测量可以优化KNN的铁电性能,然而,该体系铁电性能的原子尺度机理仍是一个亟待解决的问题.本文通过训练具有从头算精度的深度学习势(DP),对不同K/Na比例(x=0.25~1.0)的KNN展开了分子动力学模拟研究.DP模型在描述不同相结构下的KNbO_(3)和K_(0.5)Na_(0.5)NbO_(3)的状态方程、弹性常数和声子色散关系等特性上表现出极高的准确性,证实了DP模型的适用性和可扩展性.分子动力学模拟的结果表明,KNN(x=0.25~1.0)的顺电-铁电相变主要受到有序-无序效应的驱动;而位移效应在随后的铁电相变中占主导地位.特别地,随着Na含量的增加,K/Na离子半径差异会引起明显的氧八面体畸变,导致90°畴壁形成更加困难.可以预见的是,基于DP的原子模拟有助于为储能和换能材料的复杂结构、热力学和铁电特性的探索提供一个准确和高效的计算模拟解决方案.

关 键 词：KNN molecular dynamics deep potential phase transitions domain dynamics 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TB34[自动化与计算机技术—控制科学与工程]