Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning

Abstract

We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity down to the experimental glass transition temperature, $T_g$, where structural relaxation cannot be analyzed using molecular dynamics simulations. The results indicate that the strength, the geometry and the characteristic length scale of the dynamic heterogeneity evolve much more slowly near $T_g$ compared to their evolution at higher temperatures. Our results show that machine learning techniques can provide physical insights on the nature of the glass transition that cannot be gained using conventional simulation techniques.

References in corpus (45)

• Theoretical perspective on the glass transition and amorphous materials
• Testing Mode-Coupling Theory for a Supercooled Binary Lennard-Jones Mixture I: The van Hove Correlation Function
• Direct experimental evidence of a growing length scale accompanying the glass transition
• The role of local structure in dynamical arrest
• Irreversible reorganization in a supercooled liquid originates from localised soft modes
• Identifying structural flow defects in disordered solids using machine learning methods
• Models and algorithms for the next generation of glass transition studies
• Predicting the long time dynamic heterogeneity in a supercooled liquid on the basis of short time heterogeneities
• Spatial correlations in the dynamics of glassforming liquids: Experimental determination of their temperature dependence
• Spontaneous and induced dynamic fluctuations in glass-formers I: General results and dependence on ensemble and dynamics
• Non-monotonic temperature evolution of dynamic correlations in glass-forming liquids
• Predicting plasticity in disordered solids from structural indicators
• Fifth-order susceptibility unveils growth of thermodynamic amorphous order in glass-formers
• Dynamic criticality in glass-forming liquids
• Spontaneous and induced dynamic correlations in glass-formers II: Model calculations and comparison to numerical simulations
• Equilibrium sampling of hard spheres up to the jamming density and beyond
• Autonomously revealing hidden local structures in supercooled liquids
• Identification of long-lived clusters and their link to slow dynamics in a model glass former
• What does the potential energy landscape tell us about the dynamics of supercooled liquids and glasses?
• On the Study of Collective Dynamics in Supercooled Liquids through the Statistics of the Iso-Configurational Ensemble
• Thirty milliseconds in the life of a supercooled liquid
• Unveiling Dimensionality Dependence of Glassy Dynamics: 2D Infinite Fluctuation Eclipses Inherent Structural Relaxation
• Correlation of Local Order with Particle Mobility in Supercooled Liquids is Highly System Dependent
• Microscopic origin of excess wings in relaxation spectra of supercooled liquids
• Efficient swap algorithms for molecular dynamics simulations of equilibrium supercooled liquids
• Averaging local structure to predict the dynamic propensity in supercooled liquids
• Decorrelation of the static and dynamic length scales in hard-sphere glass-formers
• Analysis of a growing dynamic length scale in a glass-forming binary hard-sphere mixture
• Assessing the structural heterogeneity of supercooled liquids through community inference
• Strong geometric frustration in model glassformers
• Predicting dynamic heterogeneity in glass-forming liquids by physics-inspired machine learning
• Dynamic heterogeneities above and below the mode-coupling temperature. Evidence of a dynamic crossover
• Ultrastable metallic glasses in silico
• Divergence of Voronoi cell anisotropy vector: A threshold-free characterization of local structure in amorphous materials
• Crossovers in the dynamics of supercooled liquids probed by an amorphous wall
• Experimental Evidence for a Structural-Dynamical Transition in Trajectory Space
• Self-induced heterogeneity in deeply supercooled liquids
• Relevance of Shear Transformations in the Relaxation of Supercooled Liquids
• BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion
• Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations
• Dimensionality reduction of local structure in glassy binary mixtures
• Improving the prediction of glassy dynamics by pinpointing the local cage
• Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure
• Exploring glassy dynamics with Markov state models from graph dynamical neural networks
• Dynamic heterogeneity in two-dimensional supercooled liquids: comparison of bond-breaking and bond-orientational correlations

Cited by in corpus (7)

• Roadmap on machine learning glassy dynamics
• Normalizing flows as an enhanced sampling method for atomistic supercooled liquids
• Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism
• Fluctuations and the limit of predictability in protein evolution
• Swap Monte Carlo for diatomic molecules
• Machine learning that predicts well may not learn the correct physical descriptions of glassy systems
• Interpretability of linear regression models of glassy dynamics