Anton Bolyachkin

Partial substitution of Fe with Co in Sm(Fe,Co)₁₂-based compounds is known to enhance saturation magnetization but often causes significant coercivity degradation. To mitigate this coercivity loss, we compiled a dataset by mining published experimental data along with our in-house data and applied machine-learning techniques to search the optimal compositions. The machine learning predicts that coercivity degradation is suppressed when the Ti/V stabilizer ratio exceeds ~1.0. To validate experimentally, we prepared Sm₈Zr₂Fe_76.5-xCo_xTi₈V₅Cu_0.5(Ti-rich) and Sm₈Zr₂Fe_76.5-xCo_xTi₅V₈Cu_0.5(Ti-lean) rapidly solidified ribbons. Consistent with the machine-learning predictions, Ti-rich ribbons exhibited improved resistance to coercivity degradation compared with Ti-lean ribbons. Detailed microstructural characterization revealed that Co-doped ribbons maintained similar features, including comparable grain size and the presence of a Sm-rich intergranular phase regardless of different Ti/V ratios. However, the formation of secondary SmFe₂ phases and the increase in twinned grains after Co doping were identified as factors contributing to coercivity reduction. Moreover, evaluation of intrinsic magnetic properties showed that the anisotropy field decreased more steeply in Ti-lean alloys than in Ti-rich alloys upon Co addition, consistent with the measured tendency of coercivity values. These results demonstrate that optimizing the matrix composition, specifically maintaining Ti-rich stabilizer, is a key strategy for advancing SmFe₁₂-based permanent magnets, enabling a balance between high coercivity and magnetization for practical applications.

Many functional materials have been designed at the multiscale level. To properly simulate their physical properties, large and sophisticated computer models that can replicate microstructural features with nanometer-scale accuracy are required. This is the case for permanent magnets, which exhibit a long-standing problem of a significant offset between the simulated and experimental coercivities. To overcome this problem and resolve the Brown paradox, we propose an approach to construct large-scale finite element models based on the tomographic data from scanning electron microscopy. Our approach reconstructs a polycrystalline microstructure with actual shape, size, and packing of the grains as well as the individual regions of thin intergranular phase separated by triple junctions. Such a micromagnetic model can reproduce the experimental coercivity of ultrafine-grained Nd-Fe-B magnets along with its mechanism according to the angular dependence of coercivity. Furthermore, a remarkable role of thin triple junctions as nucleation centers for magnetization reversal is revealed. The developed digital twins of Nd-Fe-B permanent magnets can assist their optimization toward the ultimate coercivity, while the proposed tomography-based approach can be applied to a wide range of polycrystalline materials.

Many functional materials are designed at the multiscale level. To properly simulate their physical properties, large and sophisticated computer models capable of replicating the microstructure with nm-level accuracy are required. This is the case for permanent magnets, for which there is a long-standing problem of a significant offset between simulated and experimental coercivity. To overcome this problem and resolve the Brown paradox, we developed a novel approach to construct large-scale finite element models based on the microstructure tomography. It was applied to ultrafine-grained Nd-Fe-B magnets for which, besides the shape, size, and packing of the grains, we reconstructed individual regions of thin intergranular phase separated by triple junctions. Such micromagnetic model managed to reproduce not only experimental coercivity but also its mechanism according to the angular dependence of coercivity. Furthermore, a remarkable role of thin triple junctions as nucleation centers for magnetization reversal was revealed. Proposed digital twins of Nd-Fe-B permanent magnets can assist their optimization towards an ultimate coercivity, while the developed tomography-based approach can be adapted to a wide range of polycrystalline materials.