First-Principles Calculations, Machine Learning and Monte Carlo Simulations of the Magnetic Coercivity of FexCo1−x Bulks and Nanoclusters - Dou Du; Youwei Zhang; Xingwu Li; Namin Xiao (Nanomaterials)
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 (<semantics>Jij</semantics>) and magnetic anisotropy (<semantics>Ki</semantics>) along with machine learning (ML) predicted spin vectors (<semantics>Si</semantics>) serve as inputs for the Monte Carlo framework, enabling a detailed exploration of magnetic coercivity (<semantics>Hc</semantics>) across different compositions and temperatures. The simulations reveal an optimal Fe concentration, particularly around Fe0.65Co0.35, where magnetic coercivity reaches its peak, aligning with experimental trends. A similar simulation procedure was conducted for a Fe58Co32 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.