Building a “trap model” of glassy dynamics from a local structural predictor of rearrangements

Abstract Here we introduce a variation of the trap model of supercooled liquids based on softness, a particle-based variable identified by machine learning that quantifies the local structural environment and energy barrier for the particle to rearrange. As in the trap model, we assume that each par...

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Bibliographic Details
Published inEurophysics letters Vol. 144; no. 4; pp. 47001 - 47007
Main Authors Ridout, S. A., Tah, I., Liu, A. J.
Format Journal Article
LanguageEnglish
Published Les Ulis EDP Sciences, IOP Publishing and Società Italiana di Fisica 01.11.2023
IOP Publishing
Subjects
Fragility
Machine learning
Softness
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Summary:Abstract Here we introduce a variation of the trap model of supercooled liquids based on softness, a particle-based variable identified by machine learning that quantifies the local structural environment and energy barrier for the particle to rearrange. As in the trap model, we assume that each particle's softness, and hence energy barrier, evolves independently. We show that our model makes qualitatively reasonable predictions of behaviors such as the dependence of fragility on density in a model supercooled liquid. We also show failures of the model, indicating in some cases signs that softness may be missing important information, and in other cases features that may only be explained by correlations neglected in the trap model.
ISSN:0295-5075
1286-4854
DOI:10.1209/0295-5075/ad0c70