机器学习势在铁电材料研究中的应用

doi:10.11900/0412.1961.2024.00177

金属学报

2024, Vol. 60

Issue (10): 1312-1328 DOI: 10.11900/0412.1961.2024.00177

综述

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机器学习势在铁电材料研究中的应用

刘仕¹(

), 黄佳玮², 武静¹

1 西湖大学理学院物理系杭州 310030
2 香港大学机械工程系香港 999077

Application of Machine Learning Force Fields for Modeling Ferroelectric Materials

LIU Shi¹(

), HUANG Jiawei², WU Jing¹

1 Department of Physics, School of Science, Westlake University, Hangzhou 310030, China
2 Department of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China

引用本文:

刘仕, 黄佳玮, 武静. 机器学习势在铁电材料研究中的应用[J]. 金属学报, 2024, 60(10): 1312-1328.
Shi LIU, Jiawei HUANG, Jing WU. Application of Machine Learning Force Fields for Modeling Ferroelectric Materials[J]. Acta Metall Sin, 2024, 60(10): 1312-1328.

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摘要:

铁电材料作为一类具有自发极化且极化外场可控的功能材料，在非易失信息存储方面有着广阔的应用前景，同时也面临着许多挑战。铁电材料的性能与在外场作用下极化的动力学行为密切相关，在观测精度受限的实验条件下，高精度的原子级材料模拟手段显得尤为重要。分子动力学为在较大的空间和时间尺度上研究材料动力学过程提供了理想的手段，然而受制于传统力场精度差、开发难度高、可移植性差等问题，基于经典力场的分子动力学模拟在新材料上的应用受到了较大的阻碍。机器学习方法的发展为力场开发带来了新的思路。在众多机器学习势中，深度势能(DP)是一种基于深度神经网络的势能模型，具备与密度泛函理论(DFT)相媲美的精度，同时还拥有接近传统经典力场的高效计算性能。本文主要介绍了DP在铁电材料中的开发与应用，通过DP模拟，在原子尺度深入探究铁电材料中的相变机制和极化反转过程。主要工作包括重要铁电材料HfO₂、经典钙钛矿铁电材料的深度势开发和测评，基于深度势能分子动力学揭示铁电HfO₂中超高O^2-迁移率的微观机制，以及SrTiO₃极性畴界诱导的挠曲铁电和体光伏效应。

关键词 ：铁电材料, 分子动力学, 机器学习, 深度势能

Abstract：

Ferroelectric materials, which are characterized by tunable spontaneous polarization, show remarkable application potential for nonvolatile information storage; however they present various challenges. The performance of these materials is strongly influenced by their dynamic polarization behavior under multiple external fields. Due to the limited precision of experimental observations, precise atomic-level material simulations are crucial. Although molecular dynamics (MD) offers an ideal method for investigating material dynamics over a wide spatiotemporal range, its application to new materials is often limited by challenges such as low accuracy, complex development, and limited portability of conventional classical force fields. Advances in machine learning have provided new possibilities for developing force fields. Among different machine learning potentials, deep potential (DP) based on deep neural networks stands out. DP offers accuracy comparable to that of density functional theory while providing computational efficiency similar to that of conventional classical force fields. This review primarily focused on the development and application of DP in ferroelectric materials, specifically examining the phase transition mechanisms and polarization reversal processes at the atomic scale. Considerable efforts have been made to develop and evaluate DP for crucial ferroelectric materials such as hafnium dioxide (HfO₂) and classic perovskite ferroelectric materials. Furthermore, this review explores the high oxygen-ion migration kinetics in HfO₂ using DP and investigates the flexoelectricity induced by polar domain boundaries and the bulk photovoltaic effects in strontium titanate. By highlighting the use of DP molecular dynamics approaches in ferroelectric materials, this review emphasizes the role of machine learning approaches in optimizing and accelerating material simulations to facilitate further breakthroughs and discoveries.

Key words： ferroelectric material molecular dynamics machine learning deep potential

收稿日期: 2024-05-23

ZTFLH:

TM22

基金资助:国家自然科学基金项目(92370104)

通讯作者: 刘仕，liushi@westlake.edu.cn，主要从事多尺度铁电材料模拟研究

Corresponding author: LIU Shi, professor, Tel: (0571)85273989, E-mail: liushi@westlake.edu.cn

作者简介: 刘仕，男，1988年生，教授，博士
第一联系人：黄佳玮(共同第一作者)，男，1996年生，博士

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图1 以PbTiO3为代表的钙钛矿铁电材料结构示意图和铁电相HfO2结构示意图

图2 深度势能(DP)模型原理示意图[98]

图3 深度势能生成器(DP-GEN)框架图[86]

图4 DP模型预测最终训练数据集中所有结构的能量和原子力与密度泛函理论(DFT)结果对比，及绝对误差分布[103]

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]

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]

图7 基于通用原子间势模拟的温度与晶格常数的相关性[107]

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[116]

图9 SrTiO3 (STO)薄膜中畴界格点模型示意图，通过弯曲形变使极性畴界进行90°旋转的示意图，及应变下STO薄膜产生类电滞回线特征[117]

图10 铁电相HfO2的极化反转路径[128]

图11 O2-输运与铁电极化反转的成核和生长机制相耦合[128]

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