Influence of misch metal content on microstructure and magnetic properties of R–Fe–B magnets sintered by dual alloy method

MM_(14)Fe_(79.9)B_(6.1)/Nd_(13.5)Fe_(80.5)B_6 magnets were fabricated by dual alloy method(MM, misch metal). Some magnets have two Curie temperatures. Curie temperatures T_(c1)corresponds to the main phase which contains more La Ce, and T_(c1) decreases from 276.5?C to 256.6?C with the content of MM increasing from 30.3 at.% to 50.6 at.%. The variation of Br with the increase of MM indicates the existence of inter-grain exchange coupling in the magnets. When MM/R ≤ 30.3 at.%,the magnetic properties can reach the level of the intrinsic coercivity Hcj≥ 7.11 kOe and the maximum energy product(BH)max≥ 41 MGOe. Compared with Nd, La and Ce are easier to diffuse to the grain boundaries in the sintering process,and this will cause the decrease of H_(cj) Due to the diffusion between the grains, the atomic ratio of La, Ce, Pr, and Nd in each grain is different and the percentage of Nd in all grains is higher than that in misch metal.     

Composition design for (PrNd-La-Ce)_2Fe_(14)B melt-spun magnets by machine learning technique

Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity(H_(cj)) and maximum magnetic energy product(BH)_(max) via machine learning has been applied to(PrNd–La–Ce)_2Fe_(14)B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H_(cj) and(BH)_(max),with high accuracies of R~2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H_(cj) or(BH)max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2:14:1 structure melt-spun magnets.Combined with magnets' cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.     

Lorentz transmission electron microscopy studies on topological magnetic domains

Lorentz transmission electron microscopy(TEM) is a powerful tool to study the crystal structures and magnetic domain structures in correlation with novel physical properties. Nanometric topological magnetic configurations such as vortices, bubbles, and skyrmions have received enormous attention from the viewpoint of both fundamental science and potential applications in magnetic logic and memory devices, in which understanding the physical properties of magnetic nanodomains is essential. In this review article, several magnetic imaging methods in Lorentz TEM including the Fresnel and Foucault modes, electron holography, and differential phase contrast(DPC) techniques are discussed, where the novel properties of topological magnetic domains are well addressed. In addition, in situ Lorentz TEM demonstrates that the topological domains can be efficiently manipulated by electric currents, magnetic fields, and temperatures, exhibiting novel phenomena under external fields, which advances the development of topological nanodomain-based spintronics.