机器学习分子动力学辅助材料凝固形核研究进展

doi:10.11900/0412.1961.2024.00192

金属学报

2024, Vol. 60

Issue (10): 1329-1344 DOI: 10.11900/0412.1961.2024.00192

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机器学习分子动力学辅助材料凝固形核研究进展

陈名毅¹^,², 胡俊伟¹^,², 余耀辰¹^,², 牛海洋¹^,²(

)

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

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

CHEN Mingyi¹^,², HU Junwei¹^,², YU Yaochen¹^,², NIU Haiyang¹^,²(

)

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

引用本文:

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.
Mingyi CHEN, Junwei HU, Yaochen YU, Haiyang NIU. Advances in Machine Learning Molecular Dynamics to Assist Materials Nucleation and Solidification Research[J]. Acta Metall Sin, 2024, 60(10): 1329-1344.

全文: PDF(2404 KB) HTML

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。

关键词 ：凝固, 形核, 相变, 机器学习, 分子动力学, 增强采样

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.

Key words： solidification nucleation phase transition machine learning molecular dynamics enhanced sampling

收稿日期: 2024-06-05

ZTFLH:

TG111.4

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)

通讯作者: 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

Corresponding author: NIU Haiyang, professor, Tel: (029)88495240, E-mail: haiyang.niu@nwpu.edu.cn

作者简介: 陈名毅，男，1999年生，博士生

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https://www.ams.org.cn/CN/10.11900/0412.1961.2024.00192 或 https://www.ams.org.cn/CN/Y2024/V60/I10/1329

图1 机器学习势函数原理示意图

图2 机器学习方法辅助形核序参量设计[75,77]

图3 CdSe结晶机制的研究[69]

图4 Si的凝固形核过程模拟研究[66]

图5 第一性原理精度的水均质形核过程分子动力学模拟[91]

图6 Ga的凝固行为研究[98]

图7 碳酸钙球霰石相的晶体结构解析[102]

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