First-Principles Calculations of Magnetite (Fe3O4) above the Verwey Temperature by Using Self-Consistent DFT + U + V

First-Principles Calculations, Machine Learning and Monte Carlo Simulations of the Magnetic Coercivity of FexCo1-x 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 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 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.

Machine Learning Model for the Prediction of Hubbard U Parameters and Its Application to Fe-O Systems

Without incurring additional computational cost, the Hubbard model can prevalently address the electron self-interaction problems of the local or semilocal exchange-correlation functions within density functional theory. However, determining the value of the Hubbard parameter, U, promptly, efficiently, and accurately has been a long-standing challenge. Here, we develop a method for predicting the Hubbard U of iron oxides by establishing a potential relationship through machine learning fitting of structural fingerprints and the U evaluated by the linear response-constrained density functional theory method. This method performs well in calculating the properties of wüstite, hematite, and magnetite, aligning with experimental measurements or more costly hybrid functional results. Using this method, we redefine the convex hulls of the Fe-O system at 0, 50, and 100 GPa; the obtained results are in good agreement with experimental observations. We also provide insights into the debates surrounding the high-pressure phases of Fe2O3 and Fe3O4.

Debye Temperature Evaluation for Secondary Battery Cathode of α-SnxFe1-xOOH Nanoparticles Derived from the 57Fe- and 119Sn-Mössbauer Spectra

Debye temperatures of α-SnxFe1-xOOH nanoparticles (x = 0, 0.05, 0.10, 0.15 and 0.20, abbreviated as Sn100x NPs) prepared by hydrothermal reaction were estimated with 57Fe- and 119Sn-Mössbauer spectra measured by varying the temperature from 20 to 300 K. Electrical properties were studied by solid-state impedance spectroscopy (SS-IS). Together, the charge-discharge capacity of Li- and Na-ion batteries containing Sn100x NPs as a cathode were evaluated. 57Fe-Mössbauer spectra of Sn10, Sn15, and Sn20 measured at 300 K showed only one doublet due to the superparamagnetic doublet, while the doublet decomposed into a sextet due to goethite at the temperature below 50 K for Sn 10, 200 K for Sn15, and 100 K for Sn20. These results suggest that Sn10, Sn15 and Sn20 had smaller particles than Sn0. On the other hand, 20 K 119Sn-Mössbauer spectra of Sn15 were composed of a paramagnetic doublet with an isomer shift (δ) of 0.24 mm s-1 and quadrupole splitting (∆) of 3.52 mm s-1. These values were larger than those of Sn10 (δ: 0.08 mm s-1, ∆: 0.00 mm s-1) and Sn20 (δ: 0.10 mm s-1, ∆: 0.00 mm s-1), suggesting that the SnIV-O chemical bond is shorter and the distortion of octahedral SnO6 is larger in Sn15 than in Sn10 and Sn20 due to the increase in the covalency and polarization of the SnIV-O chemical bond. Debye temperatures determined from 57Fe-Mössbauer spectra measured at the low temperature were 210 K, 228 K, and 250 K for Sn10, Sn15, and Sn20, while that of α-Fe2O3 was 324 K. Similarly, the Debye temperature of 199, 251, and 269 K for Sn10, Sn15, and Sn20 were estimated from the temperature-dependent 119Sn-Mössbauer spectra, which were significantly smaller than that of BaSnO3 (=658 K) and SnO2 (=382 K). These results suggest that Fe and Sn are a weakly bound lattice in goethite NPs with low crystallinity. Modification of NPs and addition of Sn has a positive effect, resulting in an increase in DC conductivity of almost 5 orders of magnitude, from a σDC value of 9.37 × 10-7 (Ω cm)-1 for pure goethite Sn (Sn0) up to DC plateau for samples containing 0.15 and 0.20 Sn (Sn15 and Sn20) with a DC value of ~4 × 10-7 (Ω cm)-1 @423 K. This non-linear conductivity pattern and levelling at a higher Sn content suggests that structural modifications have a notable impact on electron transport, which is primarily governed by the thermally activated via three-dimensional hopping of small polarons (SPH). Measurements of SIB performance, including the Sn100x cathode under a current density of 50 mA g-1, showed initial capacities of 81 and 85 mAh g-1 for Sn0 and Sn15, which were larger than the others. The large initial capacities were measured at a current density of 5 mA g-1 found at 170 and 182 mAh g-1 for Sn15 and Sn20, respectively. It is concluded that tin-goethite NPs are an excellent material for a secondary battery cathode and that Sn15 is the best cathode among the studied Sn100x NPs.