Micromagnetics of rare-earth efficient permanent magnets

Abstract

The development of permanent magnets containing less or no rare-earth elements is linked to profound knowledge of the coercivity mechanism. Prerequisites for a promising permanent magnet material are a high spontaneous magnetization and a sufficiently high magnetic anisotropy. In addition to the intrinsic magnetic properties the microstructure of the magnet plays a significant role in establishing coercivity. The influence of the microstructure on coercivity, remanence, and energy density product can be understood by {using} micromagnetic simulations. With advances in computer hardware and numerical methods, hysteresis curves of magnets can be computed quickly so that the simulations can readily provide guidance for the development of permanent magnets. The potential of rare-earth reduced and free permanent magnets is investigated using micromagnetic simulations. The results show excellent hard magnetic properties can be achieved in grain boundary engineered NdFeB, rare-earth magnets with a ThMn12 structure, Co-based nano-wires, and L10-FeNi provided that the magnet's microstructure is optimized.

References in corpus (6)

• Electronic structure and magnetic properties of L1_0 binary alloys
• Magnetic nanowires as permanent magnet materials
• Simulation of alnico coercivity
• On the limits of coercivity in permanent magnets
• Magnetization reversal behavior in complex shaped Co nanowires: a nanomagnet morphology optimization
• Efficient Energy-minimization in Finite-Difference Micromagnetics: Speeding up Hysteresis Computations

Cited by in corpus (10)

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• MagTense: a micromagnetic framework using the analytical demagnetization tensor
• Physics-informed machine learning and stray field computation with application to micromagnetic energy minimization
• Machine learning methods for the prediction of micromagnetic magnetization dynamics
• Magnetism in Metastable and Annealed Compositionally Complex Alloys
• Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method
• Anisotropy at twin interfaces in $R{T}_{12}$ ($R$=rare earth, $T$=transition metal) magnets
• Higher order stray field computation on tensor product domains
• CuPyMag: GPU-Accelerated Finite-Element Micromagnetics with Magnetostriction