First-order phase transition in a 2D random-field Ising model with conflicting dynamics

TL;DR

This study investigates how a 2D random-field Ising model with conflicting dynamics undergoes first-order phase transitions, revealing the influence of random magnetic fields with a double-Gaussian distribution on phase behavior.

Contribution

The paper introduces a detailed analysis of a 2D random-field Ising model with a novel double-Gaussian distribution of fields, identifying conditions for first-order phase transitions and estimating critical exponents.

Findings

First-order phase transitions occur at low temperatures and high field intensities.

Finite size scaling estimates critical exponents in the continuous transition region.

Phase diagram sketch shows the influence of distribution parameters on phase boundaries.

Abstract

The effects of locally random magnetic fields are considered in a nonequilibrium Ising model defined on a square lattice with nearest-neighbors interactions. In order to generate the random magnetic fields, we have considered random variables $\{h\}$ that change randomly with time according to a double-gaussian probability distribution, which consists of two single gaussian distributions, centered at $+h_{o}$ and $-h_{o}$, with the same width $\sigma$. This distribution is very general, and can recover in appropriate limits the bimodal distribution ($\sigma\to 0$) and the single gaussian one ($ho=0$). We performed Monte Carlo simulations in lattices with linear sizes in the range $L=32 - 512$. The system exhibits ferromagnetic and paramagnetic steady states. Our results suggest the occurence of first-order phase transitions between the above-mentioned phases at low temperatures and large random-field intensities $h_{o}$, for some small values of the width $\sigma$. By means of finite size scaling, we estimate the critical exponents in the low-field region, where we have continuous phase transitions. In addition, we show a sketch of the phase diagram of the model for some values of $\sigma$.