Elasticity, Facilitation and Dynamic Heterogeneity in Glass-Forming liquids

Ozawa, Masakuni; Biroli, Giulio

doi:10.48550/arxiv.2209.08861

Cited by 1 publication

(1 citation statement)

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“…This correspondence suggests that the dynamical domains become larger than the static ones due to collective excitations of nearby mobile domains (that are expected to be indicated by low values of Γ). This picture is consistent with a recent theoretical work [58] where the dynamic heterogeneity was explained by collective behaviors of local instabilities (that correspond to mobile domains) via elastic interactions.…”

Section: Do the Coarse-graining Lengths Have A Structural Origin?supporting

confidence: 92%

What do deep neural networks find in disordered structures of glasses?

Oyama¹,

Koyama²,

Kawasaki

2023

Front. Phys.

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Glass transitions are widely observed in various types of soft matter systems. However, the physical mechanism of these transitions remains elusive despite years of ambitious research. In particular, an important unanswered question is whether the glass transition is accompanied by a divergence of the correlation lengths of the characteristic static structures. In this study, we develop a deep-neural-network-based method that is used to extract the characteristic local meso-structures solely from instantaneous particle configurations without any information about the dynamics. We first train a neural network to classify configurations of liquids and glasses correctly. Then, we obtain the characteristic structures by quantifying the grounds for the decisions made by the network using Gradient-weighted Class Activation Mapping (Grad-CAM). We consider two qualitatively different glass-forming binary systems, and through comparisons with several established structural indicators, we demonstrate that our system can be used to identify characteristic structures that depend on the details of the systems. Moreover, the extracted structures are remarkably correlated with the non-equilibrium aging dynamics in thermal fluctuations.

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Section: Do the Coarse-graining Lengths Have A Structural Origin?supporting

confidence: 92%