Electronic Structure Theory for Condensed Matter Systems

Abstract

Recent application of the first-principles electronic-structure method to magnetic and semiconductor materials is reviewed. As an application to magnetic materials, the crystal structure of a ferromagnet, Fe16N2, is optimized by minimizing total energy. An interesting cooperative structural relaxation behavior is found. Magnetic properties of Fe16N2 are discussed by analyzing the local magnetic moments and hyperfine fields at the iron and nitrogen sites. Magnetism of TPt3 (T=V, Cr, Mn, Fe, Co and Ni) intermetallics is also studied from first principles. By including the spin-orbit interaction as the second variation in a one-electron equation, the orbital magnetic moments are evaluated in addition to the spin counterparts. A microscopic mechanism for the systematic variation of the Pt-site orbital moments in the series is given and the importance of mixing effect between Pt and 3d transitionmetal d states is emphasized. As an application to semiconductor materials, exotic electronic properties of novel semiconductor nanostructures, GaAs/Ge superlattices and Si quantum wires, are predicted from firstprinciples. The detailed atomic structures and stabilities of both As-rich and Ga-rich GaAs(001) surfaces are determined from the standpoint of equilibrium energetics and theoretical simulations of scanning tunneling microscopy images. Microscopic diffusion processes of Si adatoms on a hydrogen-terminated Si(001) surface are studied and the effect of hydrogen termination on Si homoepitaxial growth is discussed. Diffusion constants, that is, pre-exponential factors and activation energies, of Ga and Al adatoms on a As-rich GaAs(001) surface are evaluated from first-principles calculations of migration potentials. On the basis of these calculated results, stochastic Monte Carlo simulations for a GaAs-AlAs binary system are performed.KeywordsScanning Tunneling Microscopy ImageMagnetic Circular DichroismSpin MomentCondense Matter SystemElectronic Structure TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.