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5 HfO2不同晶相的状态方程[103]，HfO2P21/c、Pbca和Pca21相的声子谱[103]，HfO2多种晶相间能垒计算[103]，压力为0 GPa时不同温度下O原子沿[010]方向的局域位移(d[010])的概率分布，以及温度升高驱动的晶格常数以及d[010]平均值的变化[103]

Fig.5 Equation of state for different crystalline phases of HfO2 (V is the volume of the cell. Solid lines and crosses mark the results of DFT calculations and DP model predictions, respectively)[103] (a), phonon spectra of HfO2P21/c, Pbca, and Pca21phases[103] (b), energy barrier calculations between multiple crystalline phases of HfO2 (Solid lines are DFT calculations, and hollow circles are DP predictions)[103] (c), probability distributions of the local displacement of oxygen atoms along the [010] direction (d[010]) at different temperatures for a pressure of 0 GPa (Inset shows the distribution of the displacements of oxygen atoms along the [100], [010], and [001] directions at 400 K) (d), and temperature increase-driven changes in the lattice constant and in the average value of d[010], indicating the occurrence of a phase transition (e)