机器学习势在铁电材料研究中的应用
Application of Machine Learning Force Fields for Modeling Ferroelectric Materials
机器学习势在铁电材料研究中的应用 |
| 刘仕, 黄佳玮, 武静 |
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Application of Machine Learning Force Fields for Modeling Ferroelectric Materials |
| LIU Shi, HUANG Jiawei, WU Jing |
| 图8 反铁电畸变八面体转角(φ)的示意图，深势分子动力学模拟双轴面内应变下SrTiO3 (STO)块体的相图，及STO超胞(5000个原子)在双轴面内应变(-0.8%)条件下极化和ϕ随温度变化的曲线[ |
| Fig.8 Schematic representation of the inverse ferroelectric distortion octahedral turning angle (φ) (φn is defined as the antiferrodistortive (AFD) order parameter, where the index n is the sequence number of unit cell, and θ is the rotation angle of TiO6 octahedra in each unit cell) (a), phase diagram of the SrTiO3 (STO) block under biaxial in-plane strain simulated by deep potential molecular dynamics (The ferroelectric (FE) and AFD transition temperatures at different strains are indicated by blue square dots and red dots, respectively. T represents temperature) (b), and curves of polarization and octahedral turning angle (ϕ) versus temperature for STO supercells (5000 atoms) under biaxial in-plane strain (-0.8%) (c)[ |
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