Aguilera Michel, Pino-Alarcón Sergio, Peña Francisco J, Vogel Eugenio E, Cortés Natalia, Vargas Patricio
Instituto de Física, Pontificia Universidad Católica de Valparaíso, Casilla 4950, Valparaíso 2373223, Chile.
Departamento de Física, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso 2390123, Chile.
Entropy (Basel). 2024 Dec 27;27(1):11. doi: 10.3390/e27010011.
In this work, we study the magnetocaloric effect (MCE) in a working substance corresponding to a square lattice of spins with possible orientations, known as the "-state clock model". When the -state clock model has Q≥5 possible configurations, it presents the famous Berezinskii-Kosterlitz-Thouless (BKT) phase associated with vortex states. We calculate the thermodynamic quantities using Monte Carlo simulations for even numbers, ranging from Q=2 to Q=8 spin orientations per site in a lattice. We use lattices of different sizes with N=L×L=82,162,322,642,and1282 sites, considering free boundary conditions and an external magnetic field varying between B=0 and B=1.0 in natural units of the system. By obtaining the entropy, it is possible to quantify the MCE through an isothermal process in which the external magnetic field on the spin system is varied. In particular, we find the values of that maximize the MCE depending on the lattice size and the magnetic phase transitions linked with the process. Given the broader relevance of the -state clock model in areas such as percolation theory, neural networks, and biological systems, where multi-state interactions are essential, our study provides a robust framework in applied quantum mechanics, statistical physics, and related fields.
在这项工作中,我们研究了一种工作物质中的磁热效应(MCE),该工作物质对应于具有 种可能取向的自旋正方形晶格,即所谓的“ 态时钟模型”。当 态时钟模型具有Q≥5种可能的构型时,它呈现出与涡旋态相关的著名的贝雷津斯基 - 科斯特利茨 - Thouless(BKT)相。我们使用蒙特卡罗模拟计算了热力学量,对于晶格中每个位点具有从Q = 2到Q = 8种自旋取向的偶数 。我们使用了不同大小的晶格,其位点数量N = L×L = 8²、16²、32²、64²和128²,考虑了自由边界条件以及在系统自然单位下在B = 0和B = 1.0之间变化的外部磁场。通过获得熵,可以通过在自旋系统上改变外部磁场的等温过程来量化磁热效应。特别是,我们根据晶格大小和与该过程相关的磁相变找到了使磁热效应最大化的 的值。鉴于 态时钟模型在渗流理论、神经网络和生物系统等领域具有更广泛的相关性,在这些领域中多态相互作用至关重要,我们的研究为应用量子力学、统计物理学及相关领域提供了一个强大的框架。