Naida Lacevic

Spatially heterogeneous dynamics investigated via a time-dependent four-point density correlation function

Relaxation in supercooled liquids above their glass transition and below the onset temperature of “slow” dynamics involves the correlated motion of neighboring particles. This correlated motion results in the appearance of spatially heterogeneous dynamics or “dynamical heterogeneity.” Traditional two-point time-dependent density correlation functions, while providing information about the transient “caging” of particles on cooling, are unable to provide sufficiently detailed information about correlated motion and dynamical heterogeneity. Here, we study a four-point, time-dependent density correlation function g4(r,t) and corresponding “structure factor” S4(q,t) which measure the spatial correlations between the local liquid density at two points in space, each at two different times, and so are sensitive to dynamical heterogeneity. We study g4(r,t) and S4(q,t) via molecular dynamics simulations of a binary Lennard-Jones mixture approaching the mode coupling temperature from above. We find that the correl...

Growing correlation length on cooling below the onset of caging in a simulated glass-forming liquid

We present a calculation of a fourth-order, time-dependent density correlation function that measures higher-order spatiotemporal correlations of the density of a liquid. From molecular dynamics simulations of a glass-forming Lennard-Jones liquid, we find that the characteristic length scale of this function has a maximum as a function of time which increases steadily beyond the characteristic length of the static pair correlation function g(r) in the temperature range approaching the mode coupling temperature from above. This length scale provides a measure of the spatially heterogeneous nature of the dynamics of the liquid in the alpha-relaxation regime.

Glass-forming liquids and polymers: with a little help from computational statistical physics

The past decade has seen a resurgence in the study of the glass transition, particularly using computational approaches. Advances in computer architectures and algorithms have allowed the study of larger system sizes for longer time scales, which has enabled computational scientists to explore both new aspects of supercooled liquids, glasses, and the glass transition, as well as to re-investigate old puzzles. One aspect of these systems in particular that is currently receiving a great deal of attention, and which has seen progress in part due to the use of computational statistical physics, is the elucidation of the detailed molecular motion and how this motion changes as a liquid approaches the glass transition. In particular, simulation has led to the discovery of dynamical, ordered structures within the disordered, glass-forming liquid [1–10]. It has long been known that despite the similarity in structure of a liquid and its glass, relaxation times, diffusivities and viscosities change by up to 14 orders of magnitude as a liquid is cooled through its glass transition. Why the dynamics can change so dramatically while static structure seemingly changes so little has been a long standing, open question in the field of glass research. Clearly, molecular motion becomes increasingly difficult as the temperature or spe-