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

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.

References in corpus (10)

• Theoretical perspective on the glass transition and amorphous materials
• Identifying structural flow defects in disordered solids using machine learning methods
• Vibrational modes identify soft spots in a sheared disordered packing
• Nonequilibrium glass transitions in driven and active matter
• Aging Effects in a Lennard-Jones Glass
• Corresponding States of Structural Glass Formers
• Excitations are localized and relaxation is hierarchical in glass-forming liquids
• Unified Description of Aging and Rate Effects in Yield of Glassy Solids
• Fluctuations, response and aging dynamics in a simple glass-forming liquid out of equilibrium
• A microscopic view of accelerated dynamics in deformed polymer glasses

Cited by in corpus (54)

• Machine learning and the physical sciences
• AutoAugment: Learning Augmentation Policies from Data
• Accelerated search and design of stretchable graphene kirigami using machine learning
• Autonomously revealing hidden local structures in supercooled liquids
• Machine learning determination of atomic dynamics at grain boundaries
• Disconnecting structure and dynamics in glassy thin films
• Inverse methods for design of soft materials
• Averaging local structure to predict the dynamic propensity in supercooled liquids
• Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
• Topology of vibrational modes predict plastic events in glasses
• Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function
• Mechanisms of bulk and surface diffusion in metallic glasses determined from molecular dynamics simulations
• Paths towards equilibrium in molecular systems: a mesoscopic Mpemba-like effect in water
• Roadmap on machine learning glassy dynamics
• BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion
• Anisotropic Structural Predictor in Glassy Materials
• A Microscopic Theory of Softness in Supercooled Liquids
• Machine Learning Characterization of Structural Defects in Amorphous Packings of Dimers and Ellipses
• Fragility in Glassy Liquids: A Structural Approach Based on Machine Learning
• Bypassing sluggishness: SWAP algorithm and glassiness in high dimensions
• Data-driven identification and analysis of the glass transition in polymer melts
• Machine-learning assisted quantum control in random environment
• Distance-as-time in physical aging
• Learning a Local Symmetry with Neural-Networks
• Understanding the fragile-to-strong transition in silica from microscopic dynamics
• Combining Machine Learning and Physics to Understand Glassy Systems
• On the emergence of large and complex memory effects in nonequilibrium fluids
• Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues
• Glass transition temperature of thin polymer films
• Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations
• Exploring glassy dynamics with Markov state models from graph dynamical neural networks
• Structural Signatures for Thermodynamic Stability in Vitreous Silica: Insight from Machine Learning and Molecular Dynamics Simulations
• Classifying the age of a glass based on structural properties: A machine learning approach
• The Role of Local Structure in the Enhanced Dynamics of Deformed Glasses
• Internally-stressed and positionally-disordered minimal complexes yield glasslike nonphononic excitations
• Testing theories of the glass transition with the same liquid, but many kinetic rules
• Machine learning glass caging order parameters with an artificial nested neural network
• A General Structural Order Parameter for the Amorphous Solidification of a Supercooled Liquid
• Shear induced ordering in systems with competing interactions: A machine learning study
• Building a "trap model" of glassy dynamics from a local structural predictor of rearrangements
• Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism
• Collective dynamics in a glass-former with Mari-Kurchan interactions
• Inferring the particle-wise dynamics of amorphous solids from the local structure at the jamming point
• Interplay of rearrangements, strain, and local structure during avalanche propagation
• Exploring the Relationship Between Softness and Excess Entropy in Glass-forming Systems
• Self-Supervised Learning for Glass Composition Screening
• Distribution of atomic rearrangement vectors in a metallic glass
• Machine learning that predicts well may not learn the correct physical descriptions of glassy systems
• Does mesoscopic elasticity control viscous slowing down in glassforming liquids?
• Conducting Electrons in Amorphous Si Nanostructures: Coherent Interference and Metal-Insulator Transitions Mediated by Local Structures
• Designing Machine Learning Surrogates using Outputs of Molecular Dynamics Simulations as Soft Labels
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization
• Identifying structural signatures of shear banding in model polymer nanopillars
• Machine learning analysis of dimensional reduction conjecture for nonequilibrium Berezinskii-Kosterlitz-Thouless transition in three dimensions