SearcharxivSearch

arXiv · cond-mat/0612337

Island coarsening in one-dimensional models with partially and completely reversible aggregation

Abstract

Using computer simulations and scaling ideas, we study one-dimensional models of diffusion, aggregation and detachment of particles from islands in the post-deposition regime, i. e. without flux. The diffusion of isolated particles takes place with unit rate, aggregation occurs immediately upon contact with another particle or island, and detachment from an island occurs with rate epsilon = exp(-E/kT), where E is the related energy barrier. In the partially reversible model, dissociation is limited to islands of size larger than a critical value i, while in the completely reversible model there is no restriction to that process (infinite i). Extending previous simulation results for the completely reversible case, we observe that a peaked island size distribution in the intermediate time regime, in which the mean island size is increasing, crosses over to the theoretically predicted exponentially decreasing distribution at long times. It contrasts with the partially reversible model, in which peaked distributions are obtained until the long time frozen state, which is attained with a crossover time $τ\sim \frac{i^3}ε$. The mean island size at saturation varies as $S_{sat}\approx 2i+Cε$ (C constant), while the completely reversible case shows an Ahrrenius dependence of the mean island size, $S\sim ε^{-1/2}$. Thus, for different coverages, the effect of the critical size i on the geometric features is much stronger than that of epsilon, which may be used to infer the relevance of size-dependent detachment rates in real systems and other models.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anna Chame, F. D. A. Aarao Reis. 2006-12-13. Island coarsening in one-dimensional models with partially and completely reversible aggregation. https://doi.org/10.1016/j.physa.2006.10.096

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

KEEP EXPLORING

Related papers

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

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

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