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 (12)

• Theoretical perspective on the glass transition and amorphous materials
• 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
• 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
• Spontaneous and induced dynamic correlations in glass-formers II: Model calculations and comparison to numerical simulations
• Identification of long-lived clusters and their link to slow dynamics in a model glass former
• Decorrelation of the static and dynamic length scales in hard-sphere glass-formers
• Strong geometric frustration in model glassformers
• Dimensionality reduction of local structure in glassy binary mixtures
• Improving the prediction of glassy dynamics by pinpointing the local cage

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