Estrutura dos diagramas de fases de um modelo de filmes finos via métodos de Monte Carlo

• Ano de defesa: 2010
• Autor(a) principal: Mallio, Daniel de Oliveira
• Orientador(a): Emmel, Paulo Daniel
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Física - PPGF
• País: BR
• Palavras-chave em Português: Física estatística; Filmes finos; Diagrama de fases; Ising, Modelo de; Métodos de Monte Carlo
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::FISICA
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/5037

Multilayer systems consisting of magnetic layers of heterostructures can have a variety of magnetic phases - those dependent on factors such as the exchange parameters, the geometry of the material, the temperature T to which it is submitted and the field B applied on it. Let us consider a model of Thin Film in which the various layers that compose it are formed by K Ising plans that have the same coupling constant and which has plans between K / 2 and K / 2 +1 an antiferromagnetic coupling whose interpretation would be physical defects and / or impurities in the material. The system is modeled by the following spin Hamiltonian, where the first two summations represent the interaction of first neighbors in the upper and lower layers and the third represents the interfacial antiferromagnetic coupling and the latter represents the system's interaction with the field B. The calculation of magnetic response of the model allows us to obtain temperatures and their critical fields on phase transitions of the 1st and 2nd orders allowing us to build the phase diagrams of the system. We also obtained the zero field critical exponents related to the magnetic response of the material to verify the Rushbrooke relations. We also made an analysis about the cycles of hysteresis of the material. The Monte Carlo algorithms used are the Entropic Sampling whose main idea is to exploit the principle of maximum entropy, and the usual Importance Sampling algorithm, introduced by Metropolis et al.