Relationship between local structure and relaxation in out-of-equilibrium glassy systems

Abstract

The dynamical glass transition is typically taken to be the temperature at which a glassy liquid is no longer able to equilibrate on experimental timescales. Consequently, the physical properties of these systems just above or below the dynamical glass transition, such as viscosity, can change by many orders of magnitude over long periods of time following external perturbation. During this progress toward equilibrium, glassy systems exhibit a history dependence that has complicated their study. In previous work, we bridged the gap between structure and dynamics in glassy liquids above their dynamical glass transition temperatures by introducing a scalar field called "softness," a quantity obtained using machine-learning methods. Softness is designed to capture the hidden patterns in relative particle positions that correlate strongly with dynamical rearrangements of particle positions. Here we show that the out-of-equilibrium behavior of a model glass-forming system can be understood in terms of softness. To do this we first demonstrate that the evolution of behavior following a temperature quench is a primarily structural phenomenon: The structure changes considerably, but the relationship between structure and dynamics remains invariant. We then show that the relaxation time can be robustly computed from structure as quantified by softness, with the same relation holding both in equilibrium and as the system ages. Together, these results show that the history dependence of the relaxation time in glasses requires knowledge only of the softness in addition to the usual state variables.glasses | aging | structure | machine learning I n liquids cooled quickly enough so that crystallization is avoided, the dynamics become increasingly sluggish (1-3) until τeq , the time required for the system to equilibrate, exceeds experimentally accessible timescales at what is called the dynamical glass transition temperature, Tg . At timescales shorter than τeq , quantities such as the potential energy, pressure, and so on and time correlation and response functions of these quantities depend not only on state variables such as the temperature T and density ρ, but also on the history of the system as described by its path through (T , ρ)-space (4-6). This is the situation following a rapid temperature quench from the liquid into a supercooled state. As the system evolves at a fixed temperature following the quench, the dynamics slow down, the average energy barrier height increases, and energy and pressure decrease (7-10). This process continues indefinitely if the final temperature is below Tg but stops once the system has reached equilibrium if the temperature is above Tg .In recent papers we introduced a machine-learning approach to construct a "softness" field, S (11-13). The softness of a particle quantifies its local structural environment and is designed to correlate strongly with its dynamics; the higher the particle's softness, the more likely it is to rearrange. Softness can therefore be viewed as a structural order...

Structure-property relationships from universal signatures of plasticity in disordered solids

“…However, calculation of local yield stress requires knowledge of interparticle interactions; this is often difficult to obtain in experimental systems such as colloidal and granular packings, which are naturally polydisperse. Several of us (19, 21) have shown that local structure alone can be used to develop a predictive description of dynamics in glassy liquids (19) and aging glasses (21). Central to the approach is the introduction of “softness,” a particle-based quantity that depends only on the local structural environment of the particle.…”

“…For disordered solids, structural fingerprints of rearrangements are subtle. We exploit a recently introduced, machine-learned microscopic structural quantity, “softness,” which has been shown to be strongly predictive of rearrangements in disordered solids (18) and has expanded our conceptual understanding of glassy liquids (19, 20) and aging glasses (21). We link the spatial correlations of softness to the size of rearrangements, and we link the strain response of softness to the yield strain.…”

On the emergence of large and complex memory effects in nonequilibrium fluids

“…Experimental observations reveal that the response to an excitation of complex condensed matter systems may depend on the entire system's history, and not just on the instantaneous value of the state variables [1][2][3][4][5][6][7][8]. This is usually called memory effect.…”

Anisotropic structural predictor in glassy materials

“…We then construct a structural predictor as the product of the local heat capacity and its linear response to external deformation, and show that it offers enhanced predictability of plastic rearrangements under deformation in different directions, compared to the purely scalar predictor.Introduction.-At the heart of resolving the glass mystery resides the need to quantify the disordered structures inherently associated with glasses and to relate them to glass properties and dynamics, most notably spontaneous and driven structural relaxation [1,2]. Numerous attempts to address and meet this grand challenge have been made [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], aiming at defining structural indicators with predictive powers. Achieving this goal would constitute major progress in understanding glassiness and would provide invaluable insight for developing macroscopic theories of deformation and flow of glasses.Recently accumulated evidence suggests that spatially localized soft spots are the loci of glassy relaxation, and hence are highly relevant for glass dynamics.…”