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Evidence for Localized Moment Picture in Mn-based Heusler Compounds

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 Added by Julie Karel
 Publication date 2015
  fields Physics
and research's language is English




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X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) were used to probe the oxidation state and element specific magnetic moments of Mn in Heusler compounds with different crystallographic structure. The results were compared with theoretical calculations, and it was found that in full Heusler alloys, Mn is metallic (oxidation state near 0) on both sublattices. The magnetic moment is large and localized when octahedrally coordinated by the main group element, consistent with previous theoretical work, and reduced when the main group coordination is tetrahedral. By contrast, in the half Heusler compounds the magnetic moment of the Mn atoms is large and the oxidation state is +1 or +2. The magnetic and electronic properties of Mn in full and half Heusler compounds are strongly dependent on the structure and sublattice, a fact that can be exploited to design new materials.



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The prediction of ultra-low magnetic damping in Co 2 MnZ Heusler half-metal thin-film magnets is explored in this study and the damping response is shown to be linked to the underlying electronic properties. By substituting the Z elements in high crystalline quality films (Co 2 MnZ with Z=Si, Ge, Sn, Al, Ga, Sb), electronic properties such as the minority spin band gap, Fermi energy position in the gap and spin polarization can be tuned and the consequence on magnetization dynamics analyzed. The experimental results allow us to directly explore the interplay of spin polarization, spin gap, Fermi energy position and the magnetic damping obtained in these films, together with ab initio calculation predictions. The ultra-low magnetic damping coefficients measured in the range 4.1 10-4-9 10-4 for Co 2 MnSi, Ge, Sn, Sb are the lowest values obtained on a conductive layer and offers a clear experimental demonstration of theoretical predictions on Half-Metal Magnetic Heusler compounds and a pathway for future materials design.
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74 - Qiang Gao , Ingo Opahle , 2018
Based on high throughput density functional theory calculations, we performed systematic screening for spin-gapless semiconductors (SGSs) in quaternary Heusler alloys XX 0 YZ (X, X 0 , and Y are transition metal elements without Tc, and Z is one of B, Al, Ga, In, Si, Ge, Sn, Pb, P, As, Sb, and Bi). Following the empirical rule, we focused on compounds with 21, 26, or 28 valence electrons, resulting in 12, 000 possible chemical compositions. After systematically evaluating the thermodynamic, mechanical, and dynamical stabilities, we successfully identified 70 stable SGSs, confirmed by explicit electronic structure calculations with proper magnetic ground states. It is demonstrated that all four types of SGSs can be realized, defined based on the spin characters of the bands around the Fermi energy, and the type-II SGSs show promising transport properties for spintronic applications. The effect of spin-orbit coupling is investigated, resulting in large anisotropic magnetoresistance and anomalous Nernst effects.
The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has various electronic structures, such as semi-metals, semiconductors, and a topological insulator. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity, which is an important physical parameter for controlling the thermal management of the device, requires a calculation cost of several 100 times as much as the usual density functional theory calculation. Therefore, we examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory calculation in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.
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