One-dimensional irreversible aggregation with TASEP dynamics

TL;DR

This paper introduces a one-dimensional irreversible aggregation model based on TASEP dynamics, revealing complex phase behavior and transitions through extensive simulations, with potential extensions to include cluster fragmentation.

Contribution

It presents a novel model of particle aggregation with TASEP dynamics, analyzing phase transitions and finite-size effects in a nonequilibrium setting.

Findings

Identified three stationary phases: MP, CF, and mixed.

Discovered an unusual phase transition with jump discontinuities.

Established a finite-size scaling function near the transition.

Abstract

We define and study one-dimensional model of irreversible aggregation of particles obeying a discrete-time kinetics which is a special limit of the generalized Totally Asymmetric Simple Exclusion Process (gTASEP) on open chains. The model allows for clusters of particles to translate as a whole entity one site to the right with the same probability as single particles do. A particle and a cluster, as well as two clusters, irreversibly aggregate whenever they become nearest neighbors. Nonequilibrium stationary phases appear under the balance of injection and ejection of particles. By extensive Monte Carlo simulations it is established that the phase diagram in the plane of the injection-ejection probabilities consists of three stationary phases: a multi-particle (MP) one, a completely filled (CF) phase and a 'mixed' (MP+CF) one. The transitions between these phases are: an unusual transition between MP and CF with jump discontinuity in both the bulk density and the current, a conventional first-order transition with a jump in the bulk density between MP and MP+CF, and a continuous clustering-type transition from MP to CF, which takes place throughout the MP+CF phase between them. By the data collapse method a finite-size scaling function for the current and bulk density is obtained near the unusual phase transition line. A diverging correlation length associated with that transition is identified and interpreted as the size of the largest cluster. The model allows for a future extension to account for possible cluster fragmentation.