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Structural mechanism of glass transition uncovered by unsupervised machine learning

文献类型：期刊论文

作者Yang ZY(杨增宇)5; Miao, Qing3,4; Dan, JiaKun5; Liu, MingTao5; Wang YJ(王云江)1,2
刊名ACTA MATERIALIA
出版日期2024-12-01
卷号281页码:11
关键词Glass transition Unsupervised machine learning Structural origin Superfast atoms
ISSN号1359-6454
DOI10.1016/j.actamat.2024.120410
通讯作者Yang, Zeng-Yu(zyyang_m@163.com) ; Miao, Qing(miaoqing_new@163.com) ; Wang, Yun-Jiang(yjwang@imech.ac.cn)
英文摘要Uncovering the structural origins of the ubiquitous dynamic arrest phenomenon at the glass transition has long been a challenge due to the difficulty in identifying a rational structural representation from a disordered medium. To address this challenge, we propose a novel approach based on unsupervised learning to define a set of structural fingerprints. In this approach, complex local atomic environments, ranging from short to medium range, are captured by the discretized radial distribution function and projected onto a simple two-dimensional space using a neural network-based autoencoder. This two-dimensional space is characterized by two static structural indicators, P-1 and P-2, providing a comprehensive and user-friendly representation of the mysterious "glassy structure". By employing Gaussian mixture modeling, the structural space is autonomously divided into three sections, each representing a unique cluster with similar environments. These indicators not only elucidate the glass transition but also allow for the quantitative prediction of activation barriers for local structural excitations. Furthermore, the unsupervised clustering technique can distinguish between the structural features of "hard zones" and "soft zones", as well as recently proposed superfast "liquid-like" atoms in glass. This unsupervised machine learning approach demonstrates the utility of seemingly agnostic local structure in amorphous materials, offering insights into the long-sought structural origins of the glass transition.
分类号一类
WOS关键词MEDIUM-RANGE ORDER ; BULK METALLIC-GLASS ; RELAXATION ; DYNAMICS ; DEFORMATION ; TEMPERATURE ; DUCTILE ; LIQUIDS ; MIXTURE
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDB0620103] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB0510301] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; National Natural Science Foundation of China[12072332] ; National Natural Science Foundation of China[12472112] ; National Natural Science Foundation of China[11932018] ; National Natural Science Foundation of China[12402469] ; National Natural Science Foundation of China[12402326] ; China Academy of Engineering Physics (CAEP)[YZJJZL2024003]
WOS研究方向Materials Science ; Metallurgy & Metallurgical Engineering
语种英语
WOS记录号WOS:001321869700001
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; China Academy of Engineering Physics (CAEP)
其他责任者Yang, Zeng-Yu ; Miao, Qing ; Wang, Yun-Jiang
源URL[http://dspace.imech.ac.cn/handle/311007/96877]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
3.Natl Key Lab Aerosp Phys Fluids, Mianyang 621000, Sichuan, Peoples R China;
4.China Aerodynam Res & Dev Ctr, Hyperveloc Aerodynam Inst, Mianyang 621000, Sichuan, Peoples R China;
5.China Acad Engn Phys, Inst Fluid Phys, Mianyang 621999, Sichuan, Peoples R China;
推荐引用方式
GB/T 7714		 Yang ZY,Miao, Qing,Dan, JiaKun,et al. Structural mechanism of glass transition uncovered by unsupervised machine learning[J]. ACTA MATERIALIA,2024,281:11.
APA 杨增宇,Miao, Qing,Dan, JiaKun,Liu, MingTao,&王云江.(2024).Structural mechanism of glass transition uncovered by unsupervised machine learning.ACTA MATERIALIA,281,11.
MLA 杨增宇,et al."Structural mechanism of glass transition uncovered by unsupervised machine learning".ACTA MATERIALIA 281(2024):11.

入库方式： OAI收割

来源：力学研究所

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