First-Principles Calculations, Machine Learning and Monte Carlo Simulations of the Magnetic Coercivity of Fe<sub><i>x</i></sub>Co<sub>1−<i>x</i></sub> Bulks and Nanoclusters

First-Principles Calculations, Machine Learning and Monte Carlo Simulations of the Magnetic Coercivity of Fe<sub><i>x</i></sub>Co<sub>1−<i>x</i></sub> Bulks and Nanoclusters

FeCo alloys, renowned for their exceptional magnetic properties, such as high saturation magnetization and elevated Curie temperatures, hold significant potential for various technological applications. This study combines density-functional theory (DFT) and Monte Carlo (MC) simulations to investiga...

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Bibliographic Details
Main Authors: Dou Du, Youwei Zhang, Xingwu Li, Namin Xiao
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Nanomaterials
Subjects:
FexCo1-x nanoclusters
density-functional theory
machine learning
Metropolis Monte Carlo
magnetic coercivity
Online Access:https://www.mdpi.com/2079-4991/15/8/577
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Summary:FeCo alloys, renowned for their exceptional magnetic properties, such as high saturation magnetization and elevated Curie temperatures, hold significant potential for various technological applications. This study combines density-functional theory (DFT) and Monte Carlo (MC) simulations to investigate the magnetic properties of FeCo alloys and nanoclusters. DFT-derived exchange coupling constants (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>J</mi><mrow><mi>i</mi><mi>j</mi></mrow></msub></semantics></math></inline-formula>) and magnetic anisotropy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>K</mi><mi>i</mi></msub></semantics></math></inline-formula>) along with machine learning (ML) predicted spin vectors (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mi>i</mi></msub></semantics></math></inline-formula>) serve as inputs for the Monte Carlo framework, enabling a detailed exploration of magnetic coercivity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>H</mi><mi>c</mi></msub></semantics></math></inline-formula>) across different compositions and temperatures. The simulations reveal an optimal Fe concentration, particularly around Fe<sub>0.65</sub>Co<sub>0.35</sub>, where magnetic coercivity reaches its peak, aligning with experimental trends. A similar simulation procedure was conducted for a Fe<sub>58</sub>Co<sub>32</sub> nanocluster at 300 K and 500 K, demonstrating magnetic behavior comparable to bulk materials. This integrative computational approach provides a powerful tool for simulating and understanding the magnetic properties of alloys and nanomaterials, thus aiding in the design of advanced magnetic materials.
ISSN:2079-4991

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