Fragility in Glassy Liquids: A Structural Approach Based on Machine Learning

Abstract

The rapid rise of viscosity or relaxation time upon supercooling is universal hallmark of glassy liquids. The temperature dependence of the viscosity, however, is quite non universal for glassy liquids and is characterized by the system's "fragility," with liquids with nearly Arrhenius temperature-dependent relaxation times referred to as strong liquids and those with super-Arrhenius behavior referred to as fragile liquids. What makes some liquids strong and others fragile is still not well understood. Here we explore this question in a family of glassy liquids that range from extremely strong to extremely fragile, using "softness," a structural order parameter identified by machine learning to be highly correlated with dynamical rearrangements. We use a support vector machine to identify softness as the same linear combination of structural quantities across the entire family of liquids studied. We then use softness to identify the factors controlling fragility.

References in corpus (21)

• Dynamics of Viscoplastic Deformation in Amorphous Solids
• Theoretical perspective on the glass transition and amorphous materials
• Direct experimental evidence of a growing length scale accompanying the glass transition
• Random Packings of Frictionless Particles
• Identifying structural flow defects in disordered solids using machine learning methods
• Understanding fragility in supercooled Lennard-Jones mixtures. I. Locally preferred structures
• Compressing nearly hard sphere fluids increases glass fragility
• The Relationship Between Local Structure and Relaxation in Out-of-Equilibrium Glassy Systems
• Machine learning determination of atomic dynamics at grain boundaries
• Disconnecting structure and dynamics in glassy thin films
• Equivalence of glass transition and colloidal glass transition in the hard-sphere limit
• Mapping the glassy dynamics of soft spheres onto hard-sphere behavior
• Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function
• Glass Transition in Supercooled Liquids with Medium Range Crystalline Order
• A Microscopic Theory of Softness in Supercooled Liquids
• Machine Learning Characterization of Structural Defects in Amorphous Packings of Dimers and Ellipses
• Signature of Dynamical Heterogeneity in Spatial Correlations of Particle Displacement and its Temporal Evolution in Supercooled Liquids
• Correlation of plastic events with local structure in jammed packings across spatial dimensions
• Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues
• Possible Universal Relation Between Short time $\beta$-relaxation and Long time $\alpha$-relaxation in Glass-forming Liquids
• Unifying framework for strong and fragile liquids via machine learning: a study of liquid silica

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