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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Autores principales:	Juan Camilo Zapata, Johans Restrepo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Markov chain Monte Carlo Metropolis–Hastings algorithm acceptance rate magnetic nanoparticle uniaxial magnetic-crystalline anisotropy hysteresis loops Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/8af619a4ba4f452f83a13da830029370
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spelling	oai:doaj.org-article:8af619a4ba4f452f83a13da8300293702021-11-25T17:17:18ZSelf-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles10.3390/computation91101242079-3197https://doaj.org/article/8af619a4ba4f452f83a13da8300293702021-11-01T00:00:00Zhttps://www.mdpi.com/2079-3197/9/11/124https://doaj.org/toc/2079-3197A 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 distributed. In our model, the acceptance-rate algorithm allows accepting new updates at a constant rate by means of a self-adaptive mechanism of the amplitude of Néel rotation of magnetic moments. The influence of this proposal upon the magnetic properties of our system is explored by analyzing the behavior of the magnetization versus field isotherms for a wide range of acceptance rates. Our results allows reproduction of the occurrence of both blocked and superparamagnetic states for high and low acceptance-rate values respectively, from which a link with the measurement time is inferred. Finally, the interplay between acceptance rate with temperature in hysteresis curves and the time evolution of the saturation processes is also presented and discussed.Juan Camilo ZapataJohans RestrepoMDPI AGarticleMarkov chain Monte CarloMetropolis–Hastings algorithmacceptance ratemagnetic nanoparticleuniaxial magnetic-crystalline anisotropyhysteresis loopsElectronic computers. Computer scienceQA75.5-76.95ENComputation, Vol 9, Iss 124, p 124 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Markov chain Monte Carlo Metropolis–Hastings algorithm acceptance rate magnetic nanoparticle uniaxial magnetic-crystalline anisotropy hysteresis loops Electronic computers. Computer science QA75.5-76.95
spellingShingle	Markov chain Monte Carlo Metropolis–Hastings algorithm acceptance rate magnetic nanoparticle uniaxial magnetic-crystalline anisotropy hysteresis loops Electronic computers. Computer science QA75.5-76.95 Juan Camilo Zapata Johans Restrepo Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles
description	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 distributed. In our model, the acceptance-rate algorithm allows accepting new updates at a constant rate by means of a self-adaptive mechanism of the amplitude of Néel rotation of magnetic moments. The influence of this proposal upon the magnetic properties of our system is explored by analyzing the behavior of the magnetization versus field isotherms for a wide range of acceptance rates. Our results allows reproduction of the occurrence of both blocked and superparamagnetic states for high and low acceptance-rate values respectively, from which a link with the measurement time is inferred. Finally, the interplay between acceptance rate with temperature in hysteresis curves and the time evolution of the saturation processes is also presented and discussed.
format	article
author	Juan Camilo Zapata Johans Restrepo
author_facet	Juan Camilo Zapata Johans Restrepo
author_sort	Juan Camilo Zapata
title	Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles
title_short	Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles
title_full	Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles
title_fullStr	Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles
title_full_unstemmed	Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles
title_sort	self-adaptive acceptance rate-driven markov chain monte carlo method applied to the study of magnetic nanoparticles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8af619a4ba4f452f83a13da830029370
work_keys_str_mv	AT juancamilozapata selfadaptiveacceptanceratedrivenmarkovchainmontecarlomethodappliedtothestudyofmagneticnanoparticles AT johansrestrepo selfadaptiveacceptanceratedrivenmarkovchainmontecarlomethodappliedtothestudyofmagneticnanoparticles
_version_	1718412543495503872

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

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