综述

机器学习分子动力学辅助材料凝固形核研究进展

  • 陈名毅 ,
  • 胡俊伟 ,
  • 余耀辰 ,
  • 牛海洋
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  • 1 西北工业大学 凝固技术国家重点实验室 西安 710072
    2 西北工业大学 材料学院 西安 710072
陈名毅,男,1999年生,博士生
牛海洋,haiyang.niu@nwpu.edu.cn,主要从事材料相变的多尺度计算模拟、先进分子动力学方法开发、人工智能辅助的计算材料设计

收稿日期: 2024-06-05

  修回日期: 2024-07-26

  网络出版日期: 2024-08-29

基金资助

国家自然科学基金项目(92370118,22003050);国家自然科学基金优秀青年科学基金项目(海外),及凝固技术国家重点实验室课题(2024-ZD-01)

Advances in Machine Learning Molecular Dynamics to Assist Materials Nucleation and Solidification Research

  • CHEN Mingyi ,
  • HU Junwei ,
  • YU Yaochen ,
  • NIU Haiyang
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  • 1 State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, China
    2 School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
NIU Haiyang, professor, Tel: (029)88495240, E-mail: haiyang.niu@nwpu.edu.cn

Received date: 2024-06-05

  Revised date: 2024-07-26

  Online published: 2024-08-29

Supported by

National Natural Science Foundation of China(92370118,22003050);National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas), and Research Fund of the State Key Laboratory of Solidification Proceeding (NPU) of China(2024-ZD-01)

摘要

凝固形核是材料科学、凝聚态物理等领域长盛不衰的研究热点。分子动力学以及增强采样方法为从原子尺度原位观察凝固过程微观机理、解析相变热力学与动力学性质提供了有力手段。近年来,该领域的研究者们开发了一些融合机器学习技术的先进分子动力学模拟新方法,在多种体系的形核研究中取得了一定成果。本文首先回顾了凝固形核的基本理论,并从势函数、增强采样、形核序参量3个方面介绍凝固形核模拟研究中的常用方法以及机器学习技术在其中的应用。然后,选取了几个具有代表性的体系并介绍相关方法的实际应用。最后,对机器学习分子动力学辅助材料凝固形核模拟研究领域进行了总结与展望。

本文引用格式

陈名毅 , 胡俊伟 , 余耀辰 , 牛海洋 . 机器学习分子动力学辅助材料凝固形核研究进展[J]. 金属学报, 2024 , 60(10) : 1329 -1344 . DOI: 10.11900/0412.1961.2024.00192

Abstract

Solidification nucleation is an everlasting research topic in the fields of materials science and condensed matter physics. Molecular dynamics (MD) and enhanced sampling methods provide a powerful means to observe the microscopic mechanisms of solidification processes in situ at the atomic level and to analyze the thermodynamic and kinetic properties of phase transitions. Recent advancements in MD simulations, particularly those incorporating machine learning (ML) techniques, have remarkably advanced our understanding of nucleation across different systems. This paper first reviews the basic theory of solidification nucleation and introduces common methods used in solidification nucleation simulation studies. It then delves into the application of ML techniques in three key areas: force fields, enhanced sampling, and order parameters. The paper further highlights several representative systems to demonstrate the practical applications of these methods. Finally, a summary and outlook on the future of ML-assisted MD simulations for studying material solidification were provided.

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