机器学习势在铁电材料研究中的应用

刘仕, 黄佳玮, 武静

Application of Machine Learning Force Fields for Modeling Ferroelectric Materials

LIU Shi, HUANG Jiawei, WU Jing

图11 O2-输运与铁电极化反转的成核和生长机制相耦合[128]

Fig.11 Oxygen ion transport is coupled to the nucleation and growth mechanism of the ferroelectric polarization reversal[128] (a) polar-antipolar phase cycling induced by successive SI and SA ferroelectric transitions. The highlighted marker Onp in the initial structure, δ = δ0 (defined as displacement of the Op ion relative to the top Hf plane), becomes Op after SI-2, and δ = -δ0, becomes Op after SA (b) schematic representation of stochastic nucleation in a three-dimensional bulk. The nucleus is two-dimensional in the x-z plane and its thickness corresponds to half the cell length along the y-axis of the Pca21 phase of HfO2 (c) 2D-dimensional nucleus extracted from a DPMD simulation trace of a supercell containing 28800 atoms. The profile of the nucleus was determined based on the displacement δ-value of the Op ion (d) δ profiles along the z and x directions labeled in Fig.11c