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Driven diffusive systems: how steady states depend on dynamics.

Wooseop Kwak1, D P Landau, B Schmittmann

  • 1Center for Simulational Physics, The University of Georgia, Athens, Georgia 30602-2451, USA. wkwak@hal.physast.uga.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 13, 2004
PubMed
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Nonequilibrium systems depend on dynamics. Monte Carlo simulations show different correlations for Metropolis, Glauber, and heat bath rates in a driven Ising lattice gas, indicating dynamics influence effective temperature.

Area of Science:

  • Statistical Mechanics
  • Computational Physics
  • Condensed Matter Theory

Background:

  • Nonequilibrium steady states are distinct from equilibrium systems, with dynamics playing a crucial role.
  • Understanding how different dynamic rates affect system behavior is key in driven systems.

Purpose of the Study:

  • To investigate the explicit dependence of nonequilibrium steady states on underlying dynamics.
  • To compare the effects of different Monte Carlo rates (Metropolis, Glauber, heat bath) on an Ising lattice gas driven far from equilibrium.

Main Methods:

  • Utilizing Monte Carlo simulations with Metropolis, Glauber, and heat bath algorithms.
  • Driving an Ising lattice gas model far from equilibrium using an applied electric field.
  • Analyzing structure factors, two-point correlations, and energy histograms.

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Main Results:

  • Metropolis rates yielded weaker correlations compared to Glauber and heat bath rates, suggesting a higher effective temperature.
  • Structure factors and two-point correlations were nearly identical for heat bath and Glauber rates.
  • A defined ratio, related to the Boltzmann factor, indicated a dynamics-dependent thermodynamic derivative in the driven system.

Conclusions:

  • The dynamics of simulation rates significantly impact the properties of nonequilibrium steady states.
  • Different Monte Carlo algorithms can lead to distinct observable behaviors even in the same driven system.
  • The study highlights the importance of considering simulation dynamics when interpreting results from driven statistical systems.