摘要
Metallic glasses(MGs)have an amorphous atomic arrangement,but their structure and dynamics in the nanoscale are not homogeneous.Numerous studies have confirmed that the static and dynamic heterogeneities of MGs are vital for their deformation mechanism.The“defects”in MGs are envisaged to be structurally loosely packed and dynamically active to external stimuli.To date,no definite structure-property relationship has been established to identify liquid-like“defects”in MGs.In this paper,we proposed a machine-learned“defects”from atomic trajectories rather than static structural signatures.We analyzed the atomic motion behavior at different temperatures via a k-nearest neighbors machine learning model,and quantified the dynamics of individual atoms as the machine-learned temperature.Applying this new temperature-like parameter to MGs under stress-induced flow,we can recognize which atoms respond like“liquids”to the applied loads.The evolution of liquid-like regions reveals the dynamic origin of plasticity(thermo-and acousto-plasticity)of MGs and the correlation between stress-induced heterogeneity and local environment around atoms,providing new insights into thermo-and acousto-plastic forming.
金属玻璃具有无序的原子排列,但其结构与动力学并非各处均匀.许多研究证实金属玻璃的结构与动态不均匀性对于其塑性机制至关重要.金属玻璃的"缺陷"被视为结构上疏松排布、动力学上积极响应外界刺激的区域.但迄今仍未建立明确的结构-性能关系来甄别金属玻璃中的类液缺陷.本文中,我们基于模拟原子运动轨迹并结合机器学习提出了一种不依赖于静态结构特征的缺陷.利用k近邻机器学习模型分析并预测了不同温度下的原子运动行为,建立了温度类标签-原子运动特征映射关系.应用这个"机器学习温度"参数理解金属玻璃在应力下的塑性流,识别类液区原子.类液区的演化揭示了金属玻璃塑性的动态起源(包括热塑性和超声塑性),展示了应力诱发的非均匀性和原子局域环境的关联,为热塑性成型和超声加工提供了新见解.
作者
Xiaodi Liu
Quanfeng He
Wenfei Lu
Ziqing Zhou
Jinsen Tian
Dandan Liang
Jiang Ma
Yong Yang
Jun Shen
刘晓俤;赫全锋;卢文飞;周子清;田锦森;梁丹丹;马将;杨勇;沈军(College of Mechatronics and Control Engineering,Shenzhen University,Shenzhen 518060,China;Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots,Shenzhen University,Shenzhen 518060,China;Department of Mechanical Engineering,College of Engineering,City University of Hong Kong,Kowloon Tong,Kowloon,Hong Kong SAR,China;School of Materials Science and Engineering,Tongji University,Shanghai 201804,China;Shanghai Engineering Research Center of Physical Vapor Deposition(PVD)Superhard Coating and Equipment,Shanghai Institute of Technology,Shanghai 201418,China;Department of Materials Science and Engineering,College of Engineering,City University of Hong Kong,Kowloon Tong,Kowloon,Hong Kong SAR,China)
基金
supported by the National Natural Science Foundation of China(52071217)
Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots。
