铁电性
四方晶系
相变
钛酸锶
材料科学
凝聚态物理
相图
相(物质)
人工智能
算法
物理
计算机科学
纳米技术
量子力学
薄膜
电介质
光电子学
作者
Ri He,Hongyu Wu,Linfeng Zhang,Xiaoxu Wang,Fangjia Fu,Shi Liu,Zhicheng Zhong
出处
期刊:Physical review
日期:2022-02-15
卷期号:105 (6)
被引量:26
标识
DOI:10.1103/physrevb.105.064104
摘要
Strontium titanate (SrTiO3) is regarded as an essential material for oxide electronics. One of its many remarkable features is subtle structural phase transition, driven by antiferrodistortive lattice mode, from a high-temperature cubic phase to a low-temperature tetragonal phase. Classical molecular dynamics (MD) simulation is an efficient technique to reveal atomistic features of phase transition, but its application is often limited by the accuracy of empirical interatomic potentials. Here, we develop an accurate deep potential (DP) model of SrTiO3 based on a machine learning method using data from first-principles density functional theory (DFT) calculations. The DP model has DFT-level accuracy, capable of performing efficient MD simulations and accurate property predictions. Using the DP model, we investigate the temperature-driven cubic-to-tetragonal phase transition and construct the in-plane biaxial strain-temperature phase diagram of SrTiO3. The simulations demonstrate that strain-induced ferroelectric phase is characterized by two order parameters, ferroelectric distortion and antiferrodistortion, and the ferroelectric phase transition has both displacive and order-disorder characters. This works lays the foundation for the development of accurate DP models of other complex perovskite materials.
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