Influence of interface structure on electronic properties and Schottky barriers in Fe/GaAs magnetic junctions

The electronic and magnetic properties of Fe/GaAs(001) magnetic junctions are investigated using first-principles density-functional calculations. Abrupt and intermixed interfaces are considered, and the dependence of charge transfer, magnetization profiles, Schottky barrier heights, and spin polarization of densities of states on interface structure is studied. With As-termination, an abrupt interface with Fe is favored, while Ga-terminated GaAs favors the formation of an intermixed layer with Fe. The Schottky barrier heights are particularly sensitive to the abruptness of the interface. A significant density of states in the semiconducting gap arises from metal interface states. These spin-dependent interface states lead to a significant minority spin polarization of the density of states at the Fermi level that persists well into the semiconductor, providing a channel for the tunneling of minority spins through the Schottky barrier. These interface-induced gap states and their dependence on atomic structure at the interface are discussed in connection with potential spin-injection applications.Comment: 9 pages, 9 figures, to appear in PR

Unconventional superconducting pairing symmetry induced by phonons

The possibility of non-s-wave superconductivity induced by phonons is investigated using a simple model that is inspired by Sr$_2$RuO$_4$. The model assumes a two-dimensional electronic structure, a two-dimensional spin-fluctuation spectrum, and three-dimensional electron-phonon coupling. Taken separately, each interaction favors formation of spin-singlet pairs (of s symmetry for the phonon interaction and d$_{x^2-y^2}$ symmetry for the spin interaction), but in combination, a variety of more unusual singlet and triplet states are found, depending on the interaction parameters. This may have important implications for Sr$_2$RuO$_4$, providing a plausible explanation of how the observed spin fluctuations, which clearly favor d$_{x^2-y^2}$ pairing, may still be instrumental in creating a superconducting state with a different (e.g., p-wave) symmetry. It also suggests an interpretation of the large isotope effect observed in Sr$_2$RuO$_4$. These results indicate that phonons could play a key role in establishing the order-parameter symmetry in Sr$_2$RuO$_4$, and possibly in other unconventional superconductors.Comment: 6 pages, 5 figures, submitted to Phys. Rev.

Quantum phase transition in ultrahigh mobility SiGe/Si/SiGe two-dimensional electron system

The metal-insulator transition (MIT) is an exceptional test bed for studying strong electron correlations in two dimensions in the presence of disorder. In the present study, it is found that in contrast to previous experiments on lower-mobility samples, in ultra-high mobility SiGe/Si/SiGe quantum wells the critical electron density, $n_{\text{c}}$, of the MIT becomes smaller than the density, $n_{\text{m}}$, where the effective mass at the Fermi level tends to diverge. Near the topological phase transition expected at $n_{\text{m}}$, the metallic temperature dependence of the resistance should be strengthened, which is consistent with the experimental observation of more than an order of magnitude resistance drop with decreasing temperature below $\sim1$ K.Comment: Misprints corrected. As publishe

Machine-Learning Recognition of Dzyaloshinskii-Moriya Interaction from Magnetometry

The Dzyaloshinskii-Moriya interaction (DMI), which is the antisymmetric part of the exchange interaction between neighboring local spins, winds the spin manifold and can stabilize non-trivial topological spin textures. Since topology is a robust information carrier, characterization techniques that can extract the DMI magnitude are important for the discovery and optimization of spintronic materials. Existing experimental techniques for quantitative determination of DMI, such as high-resolution magnetic imaging of spin textures and measurement of magnon or transport properties, are time consuming and require specialized instrumentation. Here we show that a convolutional neural network can extract the DMI magnitude from minor hysteresis loops, or magnetic "fingerprints" of a material. These hysteresis loops are readily available by conventional magnetometry measurements. This provides a convenient tool to investigate topological spin textures for next-generation information processing