Optimal phase space sampling for Monte Carlo simulations of Heisenberg spin systems

TY - JOUR

T1 - Optimal phase space sampling for Monte Carlo simulations of Heisenberg spin systems

AU - Alzate-Cardona, J. D.

AU - Sabogal-Suárez, D.

AU - Evans, R. F.L.

AU - Restrepo-Parra, E.

N1 - © 2019 IOP Publishing Ltd

PY - 2019/3/6

Y1 - 2019/3/6

N2 - 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.

AB - 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.

KW - Heisenberg model

KW - Monte Carlo

KW - phase space sampling

UR - http://www.scopus.com/inward/record.url?scp=85060376571&partnerID=8YFLogxK

U2 - 10.1088/1361-648X/aaf852

DO - 10.1088/1361-648X/aaf852

M3 - Article

C2 - 30540976

AN - SCOPUS:85060376571

SN - 0953-8984

VL - 31

JO - Journal of Physics Condensed Matter

JF - Journal of Physics Condensed Matter

IS - 9

M1 - 095802

ER -