Abstract
This paper refers to Monte Carlo magnetic simulations for large-scale systems. We propose scaling rules to facilitate analysis of mesoscopic objects using a relatively small amount of system nodes. In our model, each node represents a volume defined by an enlargement factor. As a consequence of this approach, the parameters describing magnetic interactions on the atomic level should also be re-scaled, taking into account the detailed thermodynamic balance as well as energetic equivalence between the real and re-scaled systems. Accuracy and efficiency of the model have been depicted through analysis of the size effects of magnetic moment configuration for various characteristic objects. As shown, the proposed scaling rules, applied to the disorder-based cluster Monte Carlo algorithm, can be considered suitable tools for designing new magnetic materials and a way to include low-level or first principle calculations in finite element Monte Carlo magnetic simulations.
Highlights
Simulations of magnetization processes have significant meaning in regards to both scientific and practical points of view [1,2,3,4,5,6]
The observed progress in technologies utilizing magnetic materials requires new magnets with unique properties optimized for different applications
The first approach is rather dedicated to continuous large-scale systems in which phenomena occurring at the atomic level are represented by some average volumetric parameters
Summary
Simulations of magnetization processes have significant meaning in regards to both scientific and practical points of view [1,2,3,4,5,6]. A permanent demand for soft and hard magnets with ultimate characteristics can be observed [7,8,9,10,11] Designing such systems should include modeling of the magnetization processes using computer simulations, which enables the searching for and testing of their properties in a pre-lab state. The MC simulation methods are based on inter-atomic properties, and modeling of mesoscopic systems results in significant consumption of computational resources. This problem can be solved using parallelization of the MC algorithm [20,21]. We propose that the rules be applied to the disorder-based cluster MC algorithm [22], formulated and developed by our team, which is a promising tool for studying and designing new magnetic materials
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