Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · cond-mat/0503508

Random close packing revisited: How many ways can we pack frictionless disks?

Ning Xu·Jerzy Blawzdziewicz·Corey S. O'Hern
View original source ↗Read source document ↗Export metadata ↓

Abstract

We create collectively jammed (CJ) packings of 50-50 bidisperse mixtures of smooth disks in 2d using an algorithm in which we successively compress or expand soft particles and minimize the total energy at each step until the particles are just at contact. We focus on small systems in 2d and thus are able to find nearly all of the collectively jammed states at each system size. We decompose the probability $P(ϕ)$ for obtaining a collectively jammed state at a particular packing fraction $ϕ$ into two composite functions: 1) the density of CJ packing fractions $ρ(ϕ)$, which only depends on geometry and 2) the frequency distribution $β(ϕ)$, which depends on the particular algorithm used to create them. We find that the function $ρ(ϕ)$ is sharply peaked and that $β(ϕ)$ depends exponentially on $ϕ$. We predict that in the infinite system-size limit the behavior of $P(ϕ)$ in these systems is controlled by the density of CJ packing fractions--not the frequency distribution. These results suggest that the location of the peak in $P(ϕ)$ when $N \to \infty$ can be used as a protocol-independent definition of random close packing.

Explore related subjects

cond-mat.stat-mechcond-mat.soft

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ning Xu, Jerzy Blawzdziewicz, Corey S. O'Hern. 2005-03-20. Random close packing revisited: How many ways can we pack frictionless disks?. https://doi.org/10.1103/physreve.71.061306

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech↗
arXiv2026

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech↗
arXiv2026

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech↗