Hard-Sphere Jamming through the Lens of Linear Optimization

Abstract

The jamming transition is ubiquitous. It is present in granular matter, colloids, glasses, and many other systems. Yet, it defines a critical point whose properties still need to be fully understood. A major breakthrough came about when the replica formalism was extended to build a mean-field theory that provides an exact description of the jamming transition of spherical particles in the infinite-dimensional limit. While such theory explains the jamming critical behavior of both soft and hard spheres, investigating the transition in finite-dimensional systems poses very difficult and different problems, in particular from the numerical point of view. Soft particles are modeled by continuous potentials; thus, their jamming point can be reached through efficient energy minimization algorithms. In contrast, the latter methods are inapplicable to hard-sphere (HS) systems since the interaction energy among the particles is always zero by construction. To overcome these difficulties, here we recast the jamming of hard spheres as a constrained optimization problem and introduce the CALiPPSO algorithm, capable of readily producing jammed HS packings without including any effective potential. This algorithm brings a HS configuration of arbitrary dimensions to its jamming point by solving a chain of linear optimization problems. We show that there is a strict correspondence between the force balance conditions of jammed packings and the properties of the optimal solutions of CALiPPSO, whence we prove analytically that our packings are always isostatic and in mechanical equilibrium. Furthermore, using extensive numerical simulations, we show that our algorithm is able to probe the complex structure of the free-energy landscape, finding qualitative agreement with mean-field predictions. We also characterize the algorithmic complexity of CALiPPSO and provide an open-source implementation of it.

arXiv commentChanged title to match the version published in PRE. Find our implementation of the CALiPPSO algorithm here: https://github.com/rdhr/CALiPPSO

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Cited by in corpus (5)

• Hyperuniformity of Maximally Random Jammed Packings of Hyperspheres Across Spatial Dimensions
• Jamming, relaxation, and memory in a structureless glass former
• Recursive Algorithm to the Centroid of Free Area for Inherent Structure and Hopping Motion in Deeply Supercooled Binary Hard Disk Systems
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• Comment on "Explicit Analytical Solution for Random Close Packing in $d=2$ and $d=3$"