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Acharyya, M. (2005) Nonequilibrium Phase Transitions in Model Ferromagnets: A Review. International Journal of Modern Physics C, 16, 1631-1670.
https://doi.org/10.1142/S0129183105008266
https://www.worldscientific.com/doi/abs/10.1142/S0129183105008266

has been cited by the following article:

  • TITLE: Universality Class of the Nonequilibrium Phase Transition in Two-Dimensional Ising Ferromagnet Driven by Propagating Magnetic Field Wave

    AUTHORS: Ajay Halder, Muktish Acharyya

    KEYWORDS: Ising Model, Dynamic Phase Transition, Monte-Carlo Simulation, Propagating Wave, Finite Size Analysis, Critical Exponents, Universality

    JOURNAL NAME: Applied Mathematics, Vol.10 No.7, July 23, 2019

    ABSTRACT: The purpose of this work is to identify the universality class of the nonequilibrium phase transition in the two-dimensional kinetic Ising ferromagnet driven by propagating magnetic field wave. To address this issue, the finite size analysis of the nonequilibrium phase transition, in two-dimensional Ising ferromagnet driven by plane propagating magnetic wave, is studied by Monte Carlo simulation. It is observed that the system undergoes a nonequilibrium dynamic phase transition from a high temperature dynamically symmetric (propagating) phase to a low temperature dynamically symmetry-broken (pinned) phase as the system is cooled below the transition temperature. This transition temperature is determined precisely by studying the fourth-order Binder Cumulant of the dynamic order parameter as a function of temperature for different system sizes (L). From the finite size analysis of dynamic order parameter and the dynamic susceptibility , we have estimated the critical exponents and (measured from the data read at the critical temperature obtained from Binder cumulant), and (measured from the peak positions of dynamic susceptibility). Our results indicate that such driven Ising ferromagnet belongs to the same universality class of the two-dimensional equilibrium Ising ferromagnet (where and ), within the limits of statistical errors.