Advances in Machine Learning Molecular Dynamics to Assist Materials Nucleation and Solidification Research
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)
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.
CHEN Mingyi , HU Junwei , YU Yaochen , NIU Haiyang . Advances in Machine Learning Molecular Dynamics to Assist Materials Nucleation and Solidification Research[J]. Acta Metall Sin, 2024 , 60(10) : 1329 -1344 . DOI: 10.11900/0412.1961.2024.00192
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