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

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