The Relationship Between Local Structure and Relaxation in Out-of-Equilibrium Glassy Systems

TL;DR

This paper demonstrates that the out-of-equilibrium relaxation behavior of glassy systems can be effectively understood and predicted using a machine-learned structural descriptor called softness, which remains invariant during structural changes.

Contribution

The study extends the concept of softness to out-of-equilibrium glassy systems, showing it can predict relaxation times and remains invariant during structural evolution after a temperature quench.

Findings

Softness correlates strongly with particle rearrangements.

Structural changes do not alter the softness-dynamics relationship.

Relaxation times can be predicted from structure using softness.

Abstract

The dynamical glass transition is typically taken to be the temperature at which a glassy liquid is no longer able to equilibrate on experimental timescales. Consequently, the physical properties of these systems just above or below the dynamical glass transition, such as viscosity, can change by many orders of magnitude over long periods of time following external perturbation. During this progress towards equilibrium, glassy systems exhibit a history dependence that has complicated their study. In previous work, we bridged the gap between structure and dynamics in glassy liquids above their dynamical glass transition temperatures by introducing a scalar field called "softness", a quantity obtained using machine learning methods. Softness is designed to capture the hidden patterns in relative particle positions that correlate strongly with dynamical rearrangements of particle positions. Here we show that the out-of-equilibrium behavior of a model glassforming system can be understood in terms of softness. To do this we first demonstrate that the evolution of behavior following a temperature quench is a primarily structural phenomenon: the structure changes considerably, but the relationship between structure and dynamics remains invariant. We then show that the history-dependent relaxation time can be robustly computed from structure as quantified by softness. Together, these results motivate the use of softness to characterize the history dependence of glasses.