Coercivity influence of nanostructure in SmCo-1:7 magnets: machine learning of high-throughput micromagnetic data

Abstract As a pinning-controlled permanent magnet, tailoring the cellular nanostructure of samarium-cobalt-based 1:7-type (SmCo-1:7) magnets remains crucial for improving magnetic performance. Jointing forward and inverse machine learning models with the high-throughput micromagnetic simulations (42,300 sets), we identify the nanostructural and magnetic features that are most effective for coercivity, combining both nucleation and pinning mechanisms. Sensitivity analyses reveal that the 1:5-phase enhances coercivity by providing high anisotropy, whereas the Z-phase strengthens pinning through fluctuations in domain-wall energy. Cu additions in the 1:5-phase significantly reduce coercivity, while Fe substitutions in the 2:17-phase modestly reduce coercivity but improve pinning locally and increase saturation magnetization. Among the features examined, magnetocrystalline misorientation emerges as the dominant factor. Finally, the framework enables inverse design of nanostructures with prescribed coercivity, thereby providing a computationally cost-effective toolkit for guiding the performance tailoring of SmCo magnets.