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Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe 3 CoB 2 , through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe 3 CoB 2 exhibits a high magnetic anisotropy ( K 1 = 1.2 MJ/m 3 ) and saturation magnetic polarization ( J s = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials. 

Heusler compounds and alloys based on them are of great recent interest because they exhibit a wide variety of spin structures, magnetic properties, and electron-transport phenomena. Their properties are tunable by alloying and we have investigated L21-orderd compound Ru2MnSn and its alloys by varying the atomic Mn:Sn composition. While antiferromagnetic ordering with a Néel temperature of 361 K was observed in Ru2MnSn, the Mn-poor Ru2Mn0.8Sn1.2 alloy exhibits properties of a diluted antiferromagnet in which there are localized regions of uncompensated Mn spins. Furthermore, a noncoplanar spin structure, evident from a topological Hall-effect contribution to the room-temperature Hall resistivity, is realized in Ru2Mn0.8Sn1.2. Our combined experimental and theoretical analysis shows that in the Ru2Mn0.8Sn1.2 alloy, the magnetic properties can be explained in terms of a noncoplanar antiferromagnetic scissor mode, which creates a small net magnetization in a magnetic field and subsequently yields a Berry curvature with a strong topological Hall effect. 

We develop an open-access database that provides a large array of datasets specialized for magnetic compounds as well as magnetic clusters. Our focus is on rare-earth-free magnets. Available datasets include (i) crystallography, (ii) thermodynamic properties, such as the formation energy, and (iii) magnetic properties that are essential for magnetic-material design. Our database features a large number of stable and metastable structures discovered through our adaptive genetic algorithm (AGA) searches. Many of these AGA structures have better magnetic properties when compared to those of the existing rare-earth-free magnets and the theoretical structures in other databases. Our database places particular emphasis on site-specific magnetic data, which are obtained by high-throughput first-principles calculations. Such site-resolved data are indispensable for machine-learning modeling. We illustrate how our data-intensive methods promote efficiency of the experimental discovery of new magnetic materials. Our database provides massive datasets that will facilitate an efficient computational screening, machine-learning-assisted design, and the experimental fabrication of new promising magnets. 

New magnetic materials for energy and information-processing applications are of paramount importance in view of significant global challenges in environmental and information security. The discovery and design of materials requires efficient computational and experimental approaches for high throughput and efficiency. When increasingly powerful computational techniques are combined with special non-equilibrium fabrication methods, the search can uncover metastable compounds with desired magnetic properties. Here we review recent results on novel Fe-, Co- and Mn-rich magnetic compounds with high magnetocrystalline anisotropy, saturation magnetization, and Curie temperature created by combining experiments, adaptive genetic algorithm searches, and advanced electronic-structure computational methods. We discuss structural and magnetic properties of such materials including Co– and/or Fe–X compounds (X = N, Si, Sn, Zr, Hf, Y, C, S, Ti, or Mn), and their prospects for practical applications.