Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles

Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles

A standard canonical Markov Chain Monte Carlo method implemented with a single-macrospin movement Metropolis dynamics was conducted to study the hysteretic properties of an ensemble of independent and non-interacting magnetic nanoparticles with uniaxial magneto-crystalline anisotropy randomly distri...

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Bibliographic Details
Main Authors: Juan Camilo Zapata, Johans Restrepo
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Computation
Subjects:
Markov chain Monte Carlo
Metropolis–Hastings algorithm
acceptance rate
magnetic nanoparticle
uniaxial magnetic-crystalline anisotropy
hysteresis loops
Online Access:https://www.mdpi.com/2079-3197/9/11/124

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https://www.mdpi.com/2079-3197/9/11/124

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