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6 模块化开发深度势(ModDP) 策略示意图[105]和UniPero 流程示意图[107]

Fig.6 Schematic diagram of modular development of deep potential (ModDP) strategy (As an example, DP-GEN is used to obtain DP models for HfO2 and ZrO2, respectively, using Hf x Zr1 - x O2 (HZO). The initial training dataset consists of the converged datasets of HfO2 and ZrO2 with the random structures of Hf x Zr1 - x O2 (x = 0.25, 0.50, and 0.75), and the converged DP model of Hf x Zr1 - x O2 is finally obtained by DP-GEN. MD—molecular dynamics, MC—Monte Carlo) (a)[105] and schematic diagram of the UniPero process (The workflow for building a generic force field for chalcogenide oxides follows the strategy of ModDP) (b)[107]