First-Principles Calculations, Machine Learning and Monte Carlo Simulations of the Magnetic Coercivity of FeCo_1-...

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 (Jij) and magnetic anisotropy (Ki) along with machine learning (ML) predicted spin vectors (Si) serve as inputs for the Monte Carlo framework, enabling a detailed exploration of magnetic coercivity (Hc) across different compositions and temperatures. The simulations reveal an optimal Fe concentration, particularly around Fe_0.65Co_0.35, 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.