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金属学报  2024, Vol. 60 Issue (10): 1439-1450    DOI: 10.11900/0412.1961.2024.00143
  研究论文 本期目录 | 过刊浏览 |
超快激光诱导Cu薄膜熔化的神经网络分子动力学研究
高天雨1,2, 曾启昱1,2(), 陈博1,2, 康冬冬1,2, 戴佳钰1,2()
1 国防科技大学 理学院 长沙 410072
2 国防科技大学 湖南省极端条件物理及应用重点实验室 长沙 410072
Neural Network Molecular Dynamics Study of Ultrafast Laser-Induced Melting of Copper Nanofilms
GAO Tianyu1,2, ZENG Qiyu1,2(), CHEN Bo1,2, KANG Dongdong1,2, DAI Jiayu1,2()
1 College of Science, National University of Defense Technology, Changsha 410072, China
2 Hunan Key Laboratory of Extreme Matter and Applications, National University of Defense Technology, Changsha 410072, China
引用本文:

高天雨, 曾启昱, 陈博, 康冬冬, 戴佳钰. 超快激光诱导Cu薄膜熔化的神经网络分子动力学研究[J]. 金属学报, 2024, 60(10): 1439-1450.
Tianyu GAO, Qiyu ZENG, Bo CHEN, Dongdong KANG, Jiayu DAI. Neural Network Molecular Dynamics Study of Ultrafast Laser-Induced Melting of Copper Nanofilms[J]. Acta Metall Sin, 2024, 60(10): 1439-1450.

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摘要: 

在飞秒激光作用下,材料将经历从凝聚态到高温高压状态、甚至是理想等离子体的剧烈结构转变。在理解这一过程的微观图像上,分子动力学模拟扮演着重要角色,但是对激光作用带来的激发态等问题,传统分子动力学描述存在原理上的困难。本工作结合温度依赖的神经网络原子间势能面与双温模型分子动力学方法,在量子精度下实现激光加热Cu薄膜的大尺度模拟,深入探讨了不同激光通量作用下的热力学状态时空演化与相变动力学,揭示了随着激光通量的增加,激光诱导的熔化动力学从非均匀熔化机制到均匀熔化机制发生转变的物理图像,为深入理解激光-物质相互作用过程提供了原子尺度的微观参考。

关键词 神经网络激光-物质相互作用熔化动力学双温模型    
Abstract

Exploring ultrafast structural transitions in materials triggered by femtosecond laser pulses—from their condensed states to high-temperature, high-pressure conditions, and potentially to ideal plasmas—is a crucial scientific endeavor with profound implications for fields such as inertial confinement fusion, metal additive manufacturing, and laser processing. These extreme conditions, which are challenging to replicate and directly observe in experiments due to temporal and spatial resolution limitations, require theoretical models and simulations to decode the underlying microscopic mechanisms. Molecular dynamics (MD) simulations, especially when paired with advanced potential energy surfaces, are effective tools for addressing these challenges. However, maintaining a balance between computational efficiency and physical accuracy, particularly when simulating excited states induced by laser interactions, remains a formidable task. In this context, neural network potential energy surfaces (NNPES) have demonstrated exceptional capability for capturing the complex interactions and properties of materials under extreme conditions, providing vital links between quantum mechanics and macroscale phenomena. Using Cu as a prototypical example, the ability of NNPES to accurately depict lattice vibrations, thermophysical properties, and complex dynamics during laser-matter interactions has been demonstrated. By seamlessly integrating NNPES with a two-temperature MD model, this study directly simulates the atomic-scale dynamics of Cu thin films subjected to intense pulsed laser irradiation. This innovative approach, which combines quantum-level accuracy with large-scale thermodynamics and detailed microstructural evolution, provides unprecedented insights into the fundamental mechanisms of laser-induced melting. Our findings reveal two distinct melting behaviors in Cu, dependent on laser fluence. At fluences near the melting threshold, a heterogeneous melting process initiated at the film surface because of the lower free energy barrier was observed. The solid-liquid interface then moves inward at velocities of tens of meters per second, requiring hundreds of picoseconds for melting to complete. Conversely, at fluences well above the threshold, Cu films experience rapid and homogeneous melting, markedly different from conventional heating-induced melting. Here, the lattice temperature almost instantaneously exceeds the thermal stability limit, leading to uniform liquid nucleation and rapid growth throughout the film, culminating in complete melting within just tens of picoseconds. This study not only illuminates the atomic-scale dynamics of laser-induced melting but also underscores the transition from heterogeneous to homogeneous melting mechanisms as a function of laser fluence. This study serves as an invaluable research tool for enhancing our understanding of laser-matter interactions and their potential applications in optimizing laser-based manufacturing processes and predicting material behavior under extreme conditions. Moreover, the reliability and versatility of NNPES set the stage for extending the research to more complex systems, including alloys and amorphous materials. This expansion fosters robust connections between microscopic theories and macroscale applications, deepening our understanding of material responses to intense laser irradiation. Future studies employing this framework could explore complex physical phenomena such as explosive boiling and material disintegration during laser ablation, offering unique atomic-scale insights that could pave the way for groundbreaking discoveries and technological advancements.

Key wordsneural network    laser-matter interaction    melting dynamics    two-temperature model
收稿日期: 2024-05-07     
ZTFLH:  O469  
基金资助:国家自然科学基金项目(12104507);湖南省科技创新领军人才项目(2021RC4026);湖南省研究生科研创新项目(CX20220070)
通讯作者: 戴佳钰,jydai@nudt.edu.cn,主要从事极端条件下物质科学研究;
曾启昱,zengqiyu@nudt.edu.cn,主要从事激光与物质相互作用的原子尺度模拟研究
Corresponding author: DAI Jiayu, professor, Tel: (0731)87001006, E-mail: jydai@nudt.edu.cn;
ZENG Qiyu, Tel: 18374847919, E-mail: zengqiyu@nudt.edu.cn
作者简介: 高天雨,男,1995年生,博士生
Iter.Lattice structureTime / psEnsemble typeTemperature / KPressure / GPaSampled configuration
0fcc0.5NPT30000
1fcc1NPT30000
2fcc2NPT30000
3fcc4NPT30000
4fcc8NPT30000
5fcc16NPT30005
6fcc4NVT200, 4007
7fcc16NVT200, 4001
8fcc2NVT50060
9fcc4NVT5005
10fcc16NVT50010
11fcc2NVT80013
12fcc4NVT8009
13fcc16NVT80060
14fcc2NVT1000, 1200, 150060
15fcc4NVT1000, 1200, 150060
16fcc16NVT1000, 1200, 150060
17fcc0.5NPT300021
18fcc1NPT300046
19fcc2NPT300060
20fcc4NPT300060
21fcc8NPT300060
22fcc16NVT30060
23fcc4NVT200, 40060
24fcc16NVT200, 40060
25fcc2NVT50060
26fcc4NVT50060
27fcc16NVT50060
28fcc2NVT80060
29fcc4NVT80060
30fcc16NVT80060
31fcc2NVT1000, 120060
32fcc4NVT1000, 120060
33fcc8NVT1000, 120060
34fcc, liquid2NVT1500, 2000, 250060, 60
35fcc, liquid4NVT1500, 2000, 250060, 60
36fcc, liquid8NVT1500, 2000, 250060, 60
37Liquid2NVT300060
表1  不同热力学条件下Cu深度势能(DP)模型的构型探索
图1  Cu的DP模型预测值和密度泛函理论(DFT)计算的能量、受力和Virial张量的对比
Itema0 / nmTm / KΔHm / (kJ·kg-1)εm / (kJ·kg-1)
Relative error / %1.120.390.520.40
DP result0.36551275 ± 25232.3 ± 11.2658.7 ± 49.1
Experiment result0.3615[48]1280[51]231.1[53]661.3[51,53-56]
表2  使用DP模型得到的Cu的晶格常数(a0)、熔点(Tm)、相变潜热(ΔHm)、熔化阈值(εm)及其与实验值[48,51,53~56]的对比
图2  温度为300 K时Cu声子谱的DP模型结果与实验结果[49]对比
图3  不同压强条件下Cu熔点的DP模型结果与实验结果[51]对比
图4  Cu的晶格比热容(cp )和电子比热容(ce)
图5  18 mJ/cm2激光通量下Cu薄膜的演化动力学过程
图6  30 mJ/cm2激光通量下Cu薄膜的演化动力学过程
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