Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning

Apparent critical phenomena, typically indicated by growing correlation lengths and dynamical slowing down, are ubiquitous in nonequilibrium systems such as supercooled liquids, amorphous solids, active matter, and spin glasses. It is often challenging to determine if such observations are related to a true second-order phase transition as in the equilibrium case or simply a crossover and even more so to measure the associated critical exponents. Here we show that the simulation results of a hard-sphere glass in three dimensions are consistent with the recent theoretical prediction of a Gardner transition, a continuous nonequilibrium phase transition. Using a hybrid molecular simulation-machine learning approach, we obtain scaling laws for both finite-size and aging effects and determine the critical exponents that traditional methods fail to estimate. Our study provides an approach that is useful to understand the nature of glass transitions and can be generalized to analyze other nonequilibrium phase transitions.

Automated summary

This study investigates the Gardner transition in a three-dimensional hard-sphere glass system using a hybrid molecular simulation-machine learning approach, providing a method to understand the nature of glass transitions. The results offer insights into critical phenomena in nonequilibrium systems and can potentially be applied to analyze other non-equilibrium phase transitions.