A cluster algorithm for Monte Carlo simulations of spin ice

Abstract

We present an algorithm for Monte Carlo simulations of a nearest-neighbor spin ice model based on its cluster representation. To assess its performance, we estimate a relaxation time, and find that, in contrast to the Metropolis algorithm, our algorithm does not develop spin-freezing. Also, to demonstrate the efficiency, we calculate the spin and charge structure factors, and observe pinch points in a high-resolution color map. We then find that Debye screening works among defects and brings about short-range correlations, and that the deconfinement transition triggered by a fugacity of defects $z$ is dictated by a singular part of the free-energy density $f_{\mathrm{s}}\propto z$.

5 pages, 5 figures

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Cited by in corpus (5)

• Effect of long-and short-range interactions on the thermodynamics of dipolar spin ice
• Policy Guided Monte Carlo: Reinforcement Learning Markov Chain Dynamics
• Spin ice thin films: Large-N theory and Monte Carlo simulations
• Fragmented spin ice and multi-$k$ ordering in rare-earth antiperovskites
• 2-Form U(1) Spin Liquids: Classical Model and Quantum Aspects