机器学习型分子力场在金属材料相变与变形领域的研究进展

doi:10.11900/0412.1961.2024.00139

金属学报

2024, Vol. 60

Issue (10): 1388-1404 DOI: 10.11900/0412.1961.2024.00139

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机器学习型分子力场在金属材料相变与变形领域的研究进展

李志尚, 赵龙, 宗洪祥(

), 丁向东

西安交通大学金属材料强度国家重点实验室西安 710049

Machine-Learning Force Fields for Metallic Materials: Phase Transformations and Deformations

LI Zhishang, ZHAO Long, ZONG Hongxiang(

), DING Xiangdong

State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China

引用本文:

李志尚, 赵龙, 宗洪祥, 丁向东. 机器学习型分子力场在金属材料相变与变形领域的研究进展[J]. 金属学报, 2024, 60(10): 1388-1404.
Zhishang LI, Long ZHAO, Hongxiang ZONG, Xiangdong DING. Machine-Learning Force Fields for Metallic Materials: Phase Transformations and Deformations[J]. Acta Metall Sin, 2024, 60(10): 1388-1404.

全文: PDF(4612 KB) HTML

摘要:

金属材料在服役过程中通常伴随着相变与形变，深入理解其背后的微观机制，对于开发能够满足国家重大需求的新型金属材料至关重要。分子动力学模拟技术，特别是机器学习型分子力场赋能的分子动力学模拟技术，作为一种新的原子模拟范式，正逐渐成为从原子尺度理解金属相变与形变的强有力工具。本文综述了近年来机器学习型分子力场在金属材料相变与形变中的研究进展。首先简述了机器学习型分子力场的基本原理与发展历程；聚焦金属材料的相变与形变过程，着重阐述了机器学习型分子力场对其相变动力学与形变微观机制在原子尺度上的理解；针对现有机器学习型分子力场在金属材料相变与形变研究中存在的壁垒进行总结，并对未来可能的发展方向提出了展望。

关键词 ：机器学习型分子力场, 相变与形变, 分子动力学模拟, 金属材料

Abstract：

A comprehensive understanding of the microscopic mechanisms underlying phase transitions and deformations in metallic materials is crucial for developing new materials that meet the nation's essential needs. Molecular dynamic simulation techniques, particularly those powered by machine-learning molecular force fields, are emerging as potent tools for unraveling atomic-scale phenomena. In this study, recent advancements in machine-learning molecular force fields were reviewed to investigate metallic phase transitions and deformations. First, the fundamental principles and evolution of machine-learning molecular force fields were introduced. Then, the phase transformation and deformation of metals were examined, providing insights into the kinetics of phase transitions and microscopic mechanisms. Finally, the challenges faced by current machine-learning molecular force fields in studying metallic phase transformations and deformations were identified, and a glimpse into future research directions was discussed.

Key words： machine learning force field phase transformation and deformation molecular dynamics simulation metallic material

收稿日期: 2024-05-08

ZTFLH:

TB31

基金资助:国家重点研发计划项目(2022YFB3707601);国家自然科学基金项目(52171011,52322103,12304026)

通讯作者: 宗洪祥，zonghust@mail.xjtu.edu.cn，主要从事金属材料相变与强韧化研究

Corresponding author: ZONG Hongxiang, professor, Tel: 15029963406, E-mail: zonghust@mail.xjtu.edu.cn

作者简介: 李志尚，男，1999年生，硕士

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图1 机器学习型分子力场(MLFFs)的构造示意图[32~34]

图2 DeepMD模型的示意图：下方的框架是其中深度神经网络的放大图；原子i所有相邻原子之间的距离矩阵{ Rij }，即环境矩阵，首先转换为描述矩阵{ Dij }，再传入到隐藏层计算原子能量Ei[66]

图3 基于图表示的原子描述符构造示意图。初始图由原子属性集V = {vi }、键属性集E = {(ek, rk, sk )}和全局状态属性 u 表示。在第一个更新步骤中更新键属性。信息从形成键的原子、状态属性和前一个键属性流向新的键属性。随后，通过这3个信息先后更新原子属性和全局状态属性。最终形成新的图[75]

图4 不同MLFFs精度与效率的互制关系[83]

图5 MLFFs在传统金属材料准静态加载过程中位错动力学研究中的应用，包括：bcc-Fe的螺位错核心结构，bcc-Fe螺位错的扭结运动模式，1/2<110>螺位错与预先存在位错的Ni/Ni3Al半共格界面相互作用的应力与反应路径的依赖关系，及第一和第二相互作用阶段能量势垒随外部应力的变化[90,91]

图6 MLFFs在高熵合金准静态加载过程中位错动力学研究中的应用[93,94]

图7 MLFFs在金属纳米多晶力学行为中的应用[97]

图8 MLFFs在金属材料动态加载变形机制研究中的应用[98,99]

图9 MLFFs在过渡金属元素Zr相变机制研究中的应用[104,106]

图10 MLFFs在过渡金属材料Zr-Nb合金相变机制研究中的应用：Zr-10Nb单晶形成局部层间扭转的分子动力学模拟及Zr-Nb合金的分子动力学模拟相图[108]

图11 MLFFs在碱金属K高压固态结构相变机制研究中的应用[117,118]

图12 MLFFs在碱金属K液态结构相变机制研究中的应用[120]

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