Improving the prediction of glassy dynamics by pinpointing the local cage

Abstract

The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To do this we introduce a method to pinpoint the cage state of the initial configuration -- i.e. the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime.

References in corpus (12)

• Theoretical perspective on the glass transition and amorphous materials
• Accurate determination of crystal structures based on averaged local bond order parameters
• Supercooled Liquids for Pedestrians
• The role of local structure in dynamical arrest
• Identifying structural flow defects in disordered solids using machine learning methods
• Autonomously revealing hidden local structures in supercooled liquids
• Compressing nearly hard sphere fluids increases glass fragility
• Averaging local structure to predict the dynamic propensity in supercooled liquids
• Assessing the structural heterogeneity of supercooled liquids through community inference
• Predicting dynamic heterogeneity in glass-forming liquids by physics-inspired machine learning
• BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion
• Dimensionality reduction of local structure in glassy binary mixtures

Cited by in corpus (11)

• Roadmap on machine learning glassy dynamics
• Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning
• Geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure
• Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations
• Classifying the age of a glass based on structural properties: A machine learning approach
• Dead or alive: Distinguishing active from passive particles using supervised learning
• Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism
• Towards interpreting the thermally activated $\beta$ dynamics in metallic glass with the structural constraint neural network
• Shape matters: Inferring the motility of confluent cells from static images
• Understanding Flow Behaviors of Supercooled Liquids by Embodying Solid-Liquid Duality at Particle Level
• Machine learning that predicts well may not learn the correct physical descriptions of glassy systems