Xu Zhang, Bei Li, Ji Zou, Hanxing Liu, Ben Xu, Kai Liu
{"title":"Deep-learning enabled atomic insights into the phase transitions and nanodomain topology of lead-free (K,Na)NbO3 ferroelectrics","authors":"Xu Zhang, Bei Li, Ji Zou, Hanxing Liu, Ben Xu, Kai Liu","doi":"10.1007/s40843-024-2999-8","DOIUrl":null,"url":null,"abstract":"<p>Lead-free K<sub><i>x</i></sub>Na<sub>1−<i>x</i></sub>NbO<sub>3</sub> (KNN) perovskites have garnered increasing attention due to their exceptional ferropiezoelectric properties, which are effectively tuned <i>via</i> 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 (<i>x</i> = 0.25∼1.0) by using <i>ab-initio</i> 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<sub>3</sub> and K<sub>0.5</sub>Nb<sub>0.5</sub>O<sub>3</sub>. Furthermore, intricate temperature-dependent phase transitions and domain formation of KNNs are extensively and quantitatively captured. Simulations indicate that for KNNs with compositions <i>x</i> 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 <i>in silico</i> explorations of complex structural, thermodynamic, and ferroelectric properties for relevant energy storage and conversion materials.</p>","PeriodicalId":773,"journal":{"name":"Science China Materials","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science China Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s40843-024-2999-8","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Lead-free KxNa1−xNbO3 (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 KNbO3 and K0.5Nb0.5O3. 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.
期刊介绍:
Science China Materials (SCM) is a globally peer-reviewed journal that covers all facets of materials science. It is supervised by the Chinese Academy of Sciences and co-sponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China. The journal is jointly published monthly in both printed and electronic forms by Science China Press and Springer. The aim of SCM is to encourage communication of high-quality, innovative research results at the cutting-edge interface of materials science with chemistry, physics, biology, and engineering. It focuses on breakthroughs from around the world and aims to become a world-leading academic journal for materials science.