Alzate-Cardona, J. D., Sabogal-Suárez, D., Evans, R. F.L. orcid.org/0000-0002-2378-8203 et al. (1 more author) (2019) Optimal phase space sampling for Monte Carlo simulations of Heisenberg spin systems. Journal of Physics Condensed Matter. 095802. ISSN 1361-648X
Abstract
We present an adaptive algorithm for the optimal phase space sampling in Monte Carlo simulations of 3D Heisenberg spin systems. Based on a golden rule of the Metropolis algorithm which states that an acceptance rate of 50% is ideal to efficiently sample the phase space, the algorithm adaptively modifies a cone-based spin update method keeping the acceptance rate close to 50%. We have assessed the efficiency of the adaptive algorithm through four different tests and contrasted its performance with that of other common spin update methods. In systems at low and high temperatures and anisotropies, the adaptive algorithm proved to be the most efficient for magnetization reversal and for the convergence to equilibrium of the thermal averages and the coercivity in hysteresis calculations. Thus, the adaptive algorithm can be used to significantly reduce the computational cost in Monte Carlo simulations of 3D Heisenberg spin systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IOP Publishing Ltd |
Keywords: | Heisenberg model,Monte Carlo,phase space sampling |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > York Institute for Materials Research The University of York > Faculty of Sciences (York) > Physics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 17 Apr 2019 14:50 |
Last Modified: | 16 Oct 2024 15:37 |
Published Version: | https://doi.org/10.1088/1361-648X/aaf852 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1088/1361-648X/aaf852 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145067 |