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Spin disorder in ferrimagnetic iron oxides critically influences their electronic and magnetic properties, particularly at the nanoscale. In this work, γ-Fe₂O₃ was investigated using several density-functional approaches (PBE, PBE+U, PBE+D4, SCAN, SCAN-L, SCAN+rVV10, r²SCAN and HSE06) to identify methods capable of accurately reproducing its structural, magnetic, and electronic characteristics. The PBE functional provided the best structural agreement with experiment, while PBE+U and HSE06 most accurately described magnetic moments of –4.03/–4.06 μB (Fetetra) and 4.16/4.14 μB (Feocta). HSE06 yielded a band gap of 2.31 eV, in good agreement with the experimental value. Spin flip energetics revealed that spin realignment at tetrahedral sites is roughly twice (or more) energetically demanding than at octahedral sites; for example, under PBE+U, the energy cost is 667 meV for Fetetra and 364–414 meV for Feocta. Density of states showed that increasing spin disorder induces Fe 3d- and O 2p-derived in-gap states and can reduce the band gap up to 1.38 eV. To reduce computational cost, the CHGNet machine-learned potential was tested. While it fails to reproduce magnetic orientations, it substantially accelerates structural optimization. Overall, the results clarify the link between spin disorder and electronic structure in iron oxides and demonstrate how DFT and machine-learning methods can be combined to efficiently model complex magnetic behavior for future research.
Keywords: DFT, quantum mechanics, iron oxides, spin flip, magnetism, machine learning, neural network© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.