Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis

In this work, the spin-transition behavior in molecular magnet was investigated via Monte Carlo simulation on Ising model with mechano-elastic interaction extension. The initial spin-arrangement took hexagonal lattice structure in two dimensions, where spin molecules situated on the hexagonal lattic...

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Main Authors: Wimalin Laosiritaworn, Yongyut Laosiritaworn
Format: Journal
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84887432562&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/48025
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-480252018-04-25T08:46:46Z Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis Wimalin Laosiritaworn Yongyut Laosiritaworn In this work, the spin-transition behavior in molecular magnet was investigated via Monte Carlo simulation on Ising model with mechano-elastic interaction extension. The initial spin-arrangement took hexagonal lattice structure in two dimensions, where spin molecules situated on the hexagonal lattice points were allowed to move under spring-type elastic interaction potential. Metropolis algorithm was used to update the spin configurations and thermal hysteresis loops were recorded to extract the hysteresis properties, such as period-average magnetization, loop area, loop width and height, as functions of parameters associated to magnetic and elastic interaction in the Hamiltonian. From the Monte Carlo results, the dependence of the hysteresis loop characteristic on magnitude of energy differences and number of available states between the low spin state and the high spin state was evident. The occurrence of the cooperative effect was notable, in agreement with previous experimental investigation, when the range of Hamiltonian parameters used is appropriate. Then all the measured hysteresis characteristic were passed to the Artificial Neural Network modeling to create extensive database of how the thermal hysteresis would respond to the change of molecular magnet Hamiltonian parameters. The scattering plots between the Artificial Neural Network and the real measured results have R-square closed to one which confirms the success of Artificial Neural Network in modeling this thermal hysteresis behavior. One is therefore allowed to use this Artificial Neural Network database as a guideline to design ultra-thin-film molecular magnet application in the future. © 2013 Elsevier Ltd. All rights reserved. 2018-04-25T08:46:46Z 2018-04-25T08:46:46Z 2013-04-01 Journal 02775387 2-s2.0-84887432562 10.1016/j.poly.2013.02.071 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84887432562&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/48025
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description In this work, the spin-transition behavior in molecular magnet was investigated via Monte Carlo simulation on Ising model with mechano-elastic interaction extension. The initial spin-arrangement took hexagonal lattice structure in two dimensions, where spin molecules situated on the hexagonal lattice points were allowed to move under spring-type elastic interaction potential. Metropolis algorithm was used to update the spin configurations and thermal hysteresis loops were recorded to extract the hysteresis properties, such as period-average magnetization, loop area, loop width and height, as functions of parameters associated to magnetic and elastic interaction in the Hamiltonian. From the Monte Carlo results, the dependence of the hysteresis loop characteristic on magnitude of energy differences and number of available states between the low spin state and the high spin state was evident. The occurrence of the cooperative effect was notable, in agreement with previous experimental investigation, when the range of Hamiltonian parameters used is appropriate. Then all the measured hysteresis characteristic were passed to the Artificial Neural Network modeling to create extensive database of how the thermal hysteresis would respond to the change of molecular magnet Hamiltonian parameters. The scattering plots between the Artificial Neural Network and the real measured results have R-square closed to one which confirms the success of Artificial Neural Network in modeling this thermal hysteresis behavior. One is therefore allowed to use this Artificial Neural Network database as a guideline to design ultra-thin-film molecular magnet application in the future. © 2013 Elsevier Ltd. All rights reserved.
format Journal
author Wimalin Laosiritaworn
Yongyut Laosiritaworn
spellingShingle Wimalin Laosiritaworn
Yongyut Laosiritaworn
Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis
author_facet Wimalin Laosiritaworn
Yongyut Laosiritaworn
author_sort Wimalin Laosiritaworn
title Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis
title_short Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis
title_full Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis
title_fullStr Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis
title_full_unstemmed Artificial Neural Network modeling of spin-transition behavior in two-dimensional molecular magnet: The learning by experiences analysis
title_sort artificial neural network modeling of spin-transition behavior in two-dimensional molecular magnet: the learning by experiences analysis
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84887432562&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/48025
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