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

¹ Department of Physics and Astronomy, University of Pennsylvania - Philadelphia, PA 19104, USA
² Department of Physics, Emory University - Atlanta, GA 30322, USA
³ Speciality Glass Division, CSIR-Central Glass and Ceramic Research Institute - Kolkata 700032, India

Received: 14 August 2023
Accepted: 14 November 2023

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.