Machine learning glass caging order parameters with an artificial nested neural network

doi:10.1039/d2sm00310d

CORC > 理论物理研究所 > 中国科学院理论物理研究所 > 理论物理所科研产出 > 理论物理所1978-2010年知识产出

	Machine learning glass caging order parameters with an artificial nested neural network
	Zhang, Kaihua ; Li, Xinyang 3,4; Jin, Yuliang 1,3,4; Jiang, Ying 5
刊名	SOFT MATTER
	2022
卷号	18 期号:33 页码:6270-6277
关键词	LIQUIDS TRANSITIONS
ISSN号	1744-683X
DOI	10.1039/d2sm00310d
英文摘要	Around a glass transition, the dynamics of a supercooled liquid dramatically slow down, exhibited by caging of particles, while the structural changes remain subtle. Alternative to recent machine learning studies searching for structural predictors of glassy dynamics, here we propose to learn directly particle caging features defined purely according to dynamics. We focus on three transitions in a simulated hard sphere glass model, the melting of ultra-stable glasses, the Gardner transition and the liquid to ordinary glass transition. Implementing the machine learning algorithm based on a two-level nested neural network, we attain not only appropriate caging order parameters for all three transitions, but also a phase classification for input samples. A finite-size scaling analysis of the phase classification results identifies the order of melting (first) and Gardner (second) transitions. A false positive is avoided, as the liquid to glass transition is indicated as a crossover, rather than a phase transition with a well-defined transition point. This study paves the way to a generic approach for learning dynamical features in glassy systems, with a minimum requirement of system-specific knowledge.
学科主题	Chemistry ; Materials Science ; Physics ; Polymer Science
语种	英语
内容类型	期刊论文
源URL	[http://ir.itp.ac.cn/handle/311006/27790]
专题	理论物理研究所_理论物理所1978-2010年知识产出
作者单位	1.Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China 2.Beihang Univ, Ctr Soft Matter Phys & Its Applicat, Beijing 100191, Peoples R China 3.Beihang Univ, Sch Chem, Beijing 100191, Peoples R China 4.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Wenzhou Inst, Wenzhou 325000, Zhejiang, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Kaihua,Li, Xinyang,Jin, Yuliang,et al. Machine learning glass caging order parameters with an artificial nested neural network[J]. SOFT MATTER,2022,18(33):6270-6277.
APA	Zhang, Kaihua,Li, Xinyang,Jin, Yuliang,&Jiang, Ying.(2022).Machine learning glass caging order parameters with an artificial nested neural network.SOFT MATTER,18(33),6270-6277.
MLA	Zhang, Kaihua,et al."Machine learning glass caging order parameters with an artificial nested neural network".SOFT MATTER 18.33(2022):6270-6277.