Application of Artificial Neural Network to Calculate Curie Temperature of Ferromagnetic Materials

Abstract

Ferromagnetic materials are now interested by researchers as they have applications in various industries. Due to the complexity of the materials, an important contribution to enhance the technological development has come from the theoretical and simulation studies especially from the Monte Carlo simulation. Nevertheless, the Monte Carlo is often limited in its performance because the computational limitations, such as the simulated system sizes and simulation times. These limitations also put a constraint on the simulation time which caps the numerical accuracy. The artificial neural network is used in this study in cooperating with the Monte Carlo simulation. The aim is to investigate the possibility in obtaining the Curie temperature of ferromagnetic Ising spin in a fine scale without an intense computational required. From the results, the extracted Curie temperature is found to agree well with those from the exact theoretical analysis which verifies the artificial neural network to be a very useful technique.