Chemical ordering suppresses large-scale electronic phase separation in doped manganites

For strongly correlated oxides, it has been a long-standing issue regarding the role of the chemical ordering of the dopants on the physical properties. Here, using unit cell by unit cell superlattice growth technique, we determine the role of chemical ordering of the Pr dopant in a colossal magnetoresistant (La1-yPry)1-xCaxMnO3 (LPCMO) system, which has been well known for its large length-scale electronic phase separation phenomena. Our experimental results show that the chemical ordering of Pr leads to marked reduction of the length scale of electronic phase separations. Moreover, compared with the conventional Pr-disordered LPCMO system, the Pr-ordered LPCMO system has a metal–insulator transition that is ~100 K higher because the ferromagnetic metallic phase is more dominant at all temperatures below the Curie temperature

Structure and magnetism of new rare- earth-free intermetallic compounds: Fe3+xCo3−xTi2 (0 ≤ x ≤ 3)

We report the fabrication of a set of new rare-earth-free magnetic compounds, which form the Fe3Co3Ti2-type hexagonal structure with P-6m2 symmetry. Neutron powder diffraction shows a significant Fe/Co anti-site mixing in the Fe3Co3Ti2 structure, which has a strong effect on the magnetocrystalline anisotropy as revealed by first-principle calculations. Increasing substitution of Fe atoms for Co in the Fe3Co3Ti2 lattice leads to the formation of Fe4Co2Ti2 , Fe5CoTi, and Fe6Ti2 with significantly improved permanent-magnet properties. A high magnetic anisotropy (13.0 Mergs/cm3) and sat- uration magnetic polarization (11.4 kG) are achieved at 10 K by altering the atomic arrangements and decreasing Fe/Co occupancy disorder

Magnetism of new metastable cobalt-nitride compounds

The search for new magnetic materials with high magnetization and magnetocrystalline anisotropy is important for a wide range of applications including information and energy processing. There is only a limited number of naturally occurring magnetic compounds that are suitable. This situation stimulates an exploration of new phases that occur far from thermal-equilibrium conditions, but their stabilization is generally inhibited due to high positive formation energies. Here a nanocluster-deposition method has enabled the discovery of a set of new non-equilibrium Co-N intermetallic compounds. The experimental search was assisted by computational methods including adaptive-genetic-algorithm and electronic- structure calculations. Conventional wisdom is that the interstitial or substitutional solubility of N in Co is much lower than that in Fe and that N in Co in equilibrium alloys does not produce materials with significant magnetization and anisotropy. By contrast, our experiments identify new Co-N compounds with favorable magnetic properties including hexagonal Co3N nanoparticles with a high saturation magnetic polarization (Js = 1.28 T or 12.8 kG) and an appreciable uniaxial magnetocrystalline anisotropy (K1 = 1.01 MJ/m3 or 10.1 Mergs/cm3). This research provides a pathway for uncovering new magnetic compounds with computational efficiency beyond the existing materials database, which is significant for future technologies

Discovering rare-earth-free magnetic materials through the development of a database

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

Magnetism of new metastable cobalt-nitride compounds

The search for new magnetic materials with high magnetization and magnetocrystalline anisotropy is important for a wide range of applications including information and energy processing. There is only a limited number of naturally occurring magnetic compounds that are suitable. This situation stimulates an exploration of new phases that occur far from thermal-equilibrium conditions, but their stabilization is generally inhibited due to high positive formation energies. Here a nanocluster-deposition method has enabled the discovery of a set of new non-equilibrium Co–N intermetallic compounds. The experimental search was assisted by computational methods including adaptive-genetic-algorithm and electronic-structure calculations. Conventional wisdom is that the interstitial or substitutional solubility of N in Co is much lower than that in Fe and that N in Co in equilibrium alloys does not produce materials with significant magnetization and anisotropy. By contrast, our experiments identify new Co–N compounds with favorable magnetic properties including hexagonal Co3N nanoparticles with a high saturation magnetic polarization (Js = 1.28 T or 12.8 kG) and an appreciable uniaxial magnetocrystalline anisotropy (K1 = 1.01 MJ m−3 or 10.1 Mergs per cm3). This research provides a pathway for uncovering new magnetic compounds with computational efficiency beyond the existing materials database, which is significant for future technologies