Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding

We show that an analogy between crowding in fluid and jammed phases of hard spheres captures the density dependence of the kissing number for a family of numerically generated jammed states. We extend this analogy to jams of mixtures of hard spheres in d = 3 dimensions and, thus, obtain an estimate of the random close packing volume fraction, phi(RCP), as a function of size polydispersity. We first consider mixtures of particle sizes with discrete distributions. For binary systems, we show agreement between our predictions and simulations using both our own results and results reported in previous studies, as well as agreement with recent experiments from the literature. We then apply our approach to systems with continuous polydispersity using three different particle size distributions, namely, the log-normal, Gamma, and truncated power-law distributions. In all cases, we observe agreement between our theoretical findings and numerical results up to rather large polydispersities for all particle size distributions when using as reference our own simulations and results from the literature. In particular, we find phi(RCP) to increase monotonically with the relative standard deviation, s(sigma), of the distribution and to saturate at a value that always remains below 1. A perturbative expansion yields a closed-form expression for phi(RCP) that quantitatively captures a distribution-independent regime for s(sigma) < 0.5. Beyond that regime, we show that the gradual loss in agreement is tied to the growth of the skewness of size distributions.

Automated summary

We investigate the density dependence of the kissing number for numerically generated jammed states by analogizing the crowding in fluid and jammed phases of hard spheres. We extend this analogy to mixtures of hard spheres in three dimensions and estimate the random close packing volume fraction, phi(RCP), for different size polydispersities. Our predictions and simulations on binary systems agree with previous studies and experimental results. We find that phi(RCP) increases with the relative standard deviation of size distributions and saturates below 1. A closed-form expression for phi(RCP) captures a distribution-independent regime for small skewness of size distributions.