Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al)_(13)-based materials

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.

High performance RE–Fe–B sintered magnets with high-content misch metal by double main phase process

Double main phase process is applied to fabricate [(Pr, Nd)1 – xMMx]13.8FebalM1.5B5.9 (x = 0.5 and 0.7;M = Cu, Al, Co, and Nb) sintered magnets with high misch metal (MM) content. In comparison to the magnets by single main phase process, the enhanced magnetic properties have been achieved. For magnets of x = 0.7, Hcj increases to 371.9 kA/m by 60.5%, and (BH)max is significantly enhanced to 253.3 kJ/m3 by 56.9%, compared with those of the single main phase magnets of the same nominal composition. In combination with minor loops and magnetic recoil curves, the property improvement of magnets with double main phase method is well explained. As a result, it is demonstrated that double main phase technology is an effective approach to improve the permanent magnetic properties of MM based sintered magnets.

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.

Magnetic properties of misch-metal partially substituted Nd–Fe–B magnets sintered by dual alloy method

The misch-metal （MM） partially substituted Nd-Fe-B sintered magnets were fabricated by the dual alloy method, and the crystal structure, microstructure, and magnetic properties were analyzed comprehensively. X-ray diffraction （XRD） reveals that the increasing content of the MM has an inconsiderable effect on the crystallographic alignment of the magnets. Grains of the two main phases are uniformly distributed, and slightly deteriorate on the grain boundary. Due to the diffusion between the adjacent grains, the MM substituted Nd-Fe-B magnets contain three types of components with different Ce/La concentrations. Moreover, the first-order reversal curve （FORC） diagram is introduced to analyze the magnetization reversal process, coercivity mechanism, and distribution of reversal field in magnetic samples. The analysis indicates that there are two major reversal components, corresponding to the two different main phases. The domain nucleation and growth are determined to be the leading mechanism in controlling the magnetization reversal processes of the magnets sintered by the dual alloy method.

Spontaneous magnetization and magnetic domain texture of strontium hexaferrite in equilibrium state

The spontaneous magnetization and equilibrium domain of SrFeOunder zero external field by micromagnetic simulation and experimental measurements were investigated.It was found that the magnetic moment distribution was extremely sensitive to the grain size of strontium hexaferrite.The critical diameter from single-domain to multi-domain can be controlled by changing the thickness and diameter,which is the key to improve the permanent magnet properties.