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 towards 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 glassforming 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 history-dependent relaxation time can be robustly computed from structure as quantified by softness. Together, these results motivate the use of softness to characterize the history dependence of glasses.
When a liquid freezes, a change in the local atomic structure marks the transition to the crystal. When a liquid is cooled to form a glass, however, no noticeable structural change marks the glass ...transition. Indeed, characteristic features of glassy dynamics that appear below an onset temperature, T_0, are qualitatively captured by mean field theory, which assumes uniform local structure at all temperatures. Even studies of more realistic systems have found only weak correlations between structure and dynamics. This raises the question: is structure important to glassy dynamics in three dimensions? Here, we answer this question affirmatively by using machine learning methods to identify a new field, that we call softness, which characterizes local structure and is strongly correlated with rearrangement dynamics. We find that the onset of glassy dynamics at T_0 is marked by the onset of correlations between softness (i.e. structure) and dynamics. Moreover, we use softness to construct a simple model of slow glassy relaxation that is in excellent agreement with our simulation results, showing that a theory of the evolution of softness in time would constitute a theory of glassy dynamics.
The quantum anomalous Hall effect (QAHE) is a fundamental quantum transport phenomenon that manifests as a quantized transverse conductance in response to a longitudinally applied electric field in ...the absence of an external magnetic field, and promises to have immense application potentials in future dissipation-less quantum electronics. Here we present a novel kinetic pathway to realize the QAHE at high temperatures by \(n\)-\(p\) codoping of three-dimensional topological insulators. We provide proof-of-principle numerical demonstration of this approach using vanadium-iodine (V-I) codoped Sb\(_2\)Te\(_3\) and demonstrate that, strikingly, even at low concentrations of \(\sim\)2\% V and \(\sim\)1\% I, the system exhibits a quantized Hall conductance, the tell-tale hallmark of QAHE, at temperatures of at least \(\sim\) 50 Kelvin, which is three orders of magnitude higher than the typical temperatures at which it has been realized so far. The proposed approach is conceptually general and may shed new light in experimental realization of high-temperature QAHE.
We use machine learning methods on local structure to identify flow defects - or regions susceptible to rearrangement - in jammed and glassy systems. We apply this method successfully to two ...disparate systems: a two dimensional experimental realization of a granular pillar under compression, and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.
