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arXiv:1604.04650

Energy degeneracies from Broad Histogram Method and Wang-Landau Sampling

Alexandre P. Lima, P.M.C. de Oliveira, Daniel Girardi

Published Apr 15, 2016• 0 citations
arXivHTMLPDF

Abstract

In this work, we present a comparative study of the accuracy provided by the Wang-Landau sampling and the Broad Histogram method to estimate de density of states of the two dimensional Ising ferromagnet. The microcanonical averages used to describe the thermodynamic behaviour and to use the Broad Histogram method were obtained using the single spin-flip Wang-Landau sampling, attempting to convergence issues and accuracy improvements. We compare the results provided by both techniques with the exact ones for thermodynamic properties and critical exponents. Our results, within the Wang-Landau sampling, reveal that the Broad Histogram approach provides a better description of the density of states for all cases analysed.

DOI
10.48550/arxiv.1604.04650

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We implement a Wang-Landau sampling technique in quantum Monte Carlo (QMC) simulations for the purpose of calculating the Rényi entanglement entropies and associated mutual information. The algorithm converges an estimate for an analog to the density of states for stochastic series expansion QMC, allowing a direct calculation of Rényi entropies without explicit thermodynamic integration. We benchmark results for the mutual information on two-dimensional (2D) isotropic and anisotropic Heisenberg models, a 2D transverse field Ising model, and a three-dimensional Heisenberg model, confirming a critical scaling of the mutual information in cases with a finite-temperature transition. We discuss the benefits and limitations of broad sampling techniques compared to standard importance sampling methods.
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The concept of the partition function zeros is one of the most efficient methods for investigating the phase transitions and the critical phenomena in various physical systems. Estimating the partition function zeros requires information on the density of states Ω( E ) as a function of the energy E . Currently, the Wang-Landau Monte Carlo algorithm is one of the best methods for calculating Ω( E ). The partition function zeros in the complex temperature plane of the Ising model on an L × L square lattice ( L = 10 ~ 80) with a periodic boundary condition have been estimated by using the Wang-Landau Monte Carlo algorithm. The efficiency of the Wang-Landau Monte Carlo algorithm and the accuracies of the partition function zeros have been evaluated for three different, 5%, 10%, and 20%, flatness criteria for the histogram H ( E ).
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We briefly review some applications of the critical minimum energy-subspace method (CrMES) using the Wang–Landau sampling for the estimation of the density of states (DOS). These applications concern the twoand three-dimensional Ising models and their important conserved order parameter versions (COP), known also as the Ising models with fixed magnetization (IMFM). The recently developed CrMES scheme greatly facilitates methods for sampling the DOS of classical statistical models in large systems. In effect, the CrMES technique enables the estimation of critical behaviour using only a small part of the energy space. Specific heat curves are obtained, their peaks are located and their scaling behaviour is studied and compared with the results known from literature whenever such results exist.