Machine learning glass caging order parameters with an artificial nested neural network

Abstract

Around a glass transition, the dynamics of a supercooled liquid dramatically slow down, exhibited by caging of particles, while the structural changes remain subtle. In alternative to recent machine learning studies searching for structural predictors of glassy dynamics, here we propose to learn directly particle caging features defined purely according to dynamics. We focus on three transitions in a simulated hard sphere glass model, the melting of ultra-stable glasses, the Gardner transition and the liquid to ordinary glass transition. Implementing the machine learning algorithm based on a two-level nested neural network, we attain not only proper caging order parameters for all three transitions, but also a phase classification for input samples. A finite-size scaling analysis of the phase classification results identifies the order of melting (first) and Gardner (second) transitions. A false positive is avoided, as the liquid to glass transition is indicated as a crossover, rather than a phase transition with a well-defined transition point. This study paves the way to a generic approach for learning dynamical features in glassy systems, with a minimum requirement of system-specific knowledge.

arXiv comment19 pages, 21 figures

References in corpus (24)

• Double-slit photoelectron interference in strong-field ionization of the neon dimer
• Machine learning phases of matter
• Machine learning for molecular simulation
• Mean field theory of hard sphere glasses and jamming
• Fractal free energy landscapes in structural glasses
• The role of local structure in dynamical arrest
• Identifying structural flow defects in disordered solids using machine learning methods
• Mode-Coupling Theory (MCT) Lecture Notes
• Glass and Jamming Transitions: From Exact Results to Finite-Dimensional Descriptions
• A random critical point separates brittle and ductile yielding transitions in amorphous materials
• Roles of icosahedral and crystal-like order in hard spheres glass transition
• Equilibrium sampling of hard spheres up to the jamming density and beyond
• Autonomously revealing hidden local structures in supercooled liquids
• Growing timescales and lengthscales characterizing vibrations of amorphous solids
• The Relationship Between Local Structure and Relaxation in Out-of-Equilibrium Glassy Systems
• Averaging local structure to predict the dynamic propensity in supercooled liquids
• Assessing the structural heterogeneity of supercooled liquids through community inference
• Evidence for a disordered critical point in a glass-forming liquid
• Numerical detection of the Gardner transition in a mean-field glass former
• Nontrivial critical fixed point for replica-symmetry-breaking transitions
• Tetrahedrality dictates dynamics in hard spheres
• The melting of stable glasses is governed by nucleation-and-growth dynamics
• Determining the nonequilibrium criticality of a Gardner transition via a hybrid study of molecular simulations and machine learning
• Multiple glass transitions and higher order replica symmetry breaking of binary mixtures

Cited by in corpus (2)

• Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism
• Jamming is a first-order transition with quenched disorder in amorphous materials sheared by cyclic quasistatic deformations