原子间势
铁电性
分子动力学
机器学习
声子
统计物理学
相变
算法
材料科学
人工智能
物理
计算机科学
凝聚态物理
量子力学
电介质
作者
Hao‐Cheng Thong,XiaoYang Wang,Jiawei Han,Linfeng Zhang,Bei Li,Ke Wang,Ben Xu
出处
期刊:Physical review
日期:2023-01-05
卷期号:107 (1)
被引量:5
标识
DOI:10.1103/physrevb.107.014101
摘要
Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics.
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