Autonomously revealing hidden local structures in supercooled liquids

Abstract

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids and glasses. The conundrum: close to the glass transition, the dynamics slow down dramatically and become heterogeneous while the structure appears largely unperturbed. Largely unperturbed, however, is not the same as unperturbed, and many studies have attempted to identify "slow" local structures by exploiting dynamical information. Nonetheless, the question remains open: is the key to the slow dynamics imprinted in purely structural information? And if so, is there a way to determine the relevant structures without any dynamical information? Here, we use a newly developed unsupervised machine learning (UML) algorithm to identify structural heterogeneities in three archetypical glass formers. In each system, the UML approach autonomously designs an order parameter based purely on structural variation within a single snapshot. Impressively, this order parameter strongly correlates with the dynamical heterogeneity. Moreover, the structural characteristics linked to slow particles disappear as we move away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials.

References in corpus (27)

• Double-slit photoelectron interference in strong-field ionization of the neon dimer
• Testing Mode-Coupling Theory for a Supercooled Binary Lennard-Jones Mixture I: The van Hove Correlation Function
• Accurate determination of crystal structures based on averaged local bond order parameters
• 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
• Predicting the long time dynamic heterogeneity in a supercooled liquid on the basis of short time heterogeneities
• Roles of icosahedral and crystal-like order in hard spheres glass transition
• Growing length and time scales in glass forming liquids
• Identification of structure in condensed matter with the topological cluster classification
• Liquid Limits: The Glass Transition and Liquid-Gas Spinodal Boundaries of Metastable Liquids
• Nonperturbative effect of attractive forces in viscous liquids
• The Relationship Between Local Structure and Relaxation in Out-of-Equilibrium Glassy Systems
• Structure and dynamics in glass-formers: predictability at large length scales
• 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?
• A parameter-free, solid-angle based, nearest-neighbor algorithm
• Correlation of Local Order with Particle Mobility in Supercooled Liquids is Highly System Dependent
• Unsupervised learning for local structure detection in colloidal systems
• Does a growing static length scale control the glass transition?
• Assessing the structural heterogeneity of supercooled liquids through community inference
• Growing static and dynamic length scales in a glass-forming liquid
• Can the glass transition be explained without a growing static length scale?
• Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function
• Tetrahedrality dictates dynamics in hard spheres
• The nature of geometric frustration in the Kob-Andersen mixture
• Structural-dynamical transition in the Wahnström mixture

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• Solid-that-flows picture of glass-forming liquids
• Robust Prediction of Force Chains in Jammed Solids using Graph Neural Networks
• Relevance of Shear Transformations in the Relaxation of Supercooled Liquids
• Comparing machine learning techniques for predicting glassy dynamics
• BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion
• Soft Metamaterials: Adaptation and Intelligence
• Roadmap on machine learning glassy dynamics
• Dimensionality reduction of local structure in glassy binary mixtures
• Improving the prediction of glassy dynamics by pinpointing the local cage
• Data-driven identification and analysis of the glass transition in polymer melts
• Johari-Goldstein $\beta$ relaxation in glassy dynamics originates from two-scale energy landscape
• Many-body correlations are non-negligible in both fragile and strong glassformers
• Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning
• Correlation of plastic events with local structure in jammed packings across spatial dimensions
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
• 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
• Unsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations
• Exploring glassy dynamics with Markov state models from graph dynamical neural networks
• Dynamical Susceptibilities Near Ideal Glass Transitions
• Machine learning glass caging order parameters with an artificial nested neural network
• Selecting Relevant Structural Features for Glassy Dynamics by Information Imbalance
• Investigation of the degree of local structural similarity between the parent-liquid and children-crystal states for a model soft matter system
• A deep learning approach to the measurement of long-lived memory kernels from Generalised Langevin Dynamics
• Direct detection of plasticity onset through total-strain profile evolution
• 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
• In search of a precursor for crystal nucleation of hard and charged colloids
• Correlation between plastic rearrangements and local structure in a cyclically driven glass
• Revisiting the single-saddle model for the $\beta$-relaxation of supercooled liquids
• Glass Transition in Monolayers of Rough Colloidal Ellipsoids
• partycls: A Python package for structural clustering
• Towards interpreting the thermally activated $\beta$ dynamics in metallic glass with the structural constraint neural network
• On the geometry of topological defects in glasses
• Computational Methods toward Ultrastable Glasses
• Machine learning analysis of dimensional reduction conjecture for nonequilibrium Berezinskii-Kosterlitz-Thouless transition in three dimensions
• Unsupervised classification of disordered patterns in an oppositely charged colloidal system
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization
• Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures
• Static self-induced heterogeneity in glass-forming liquids: Overlap as a microscope
• Machine learning evaluation of structural descriptors for supercooled water