37,966 research outputs found

    A machine learning route between band mapping and band structure

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    The electronic band structure (BS) of solid state materials imprints the multidimensional and multi-valued functional relations between energy and momenta of periodically confined electrons. Photoemission spectroscopy is a powerful tool for its comprehensive characterization. A common task in photoemission band mapping is to recover the underlying quasiparticle dispersion, which we call band structure reconstruction. Traditional methods often focus on specific regions of interests yet require extensive human oversight. To cope with the growing size and scale of photoemission data, we develop a generic machine-learning approach leveraging the information within electronic structure calculations for this task. We demonstrate its capability by reconstructing all fourteen valence bands of tungsten diselenide and validate the accuracy on various synthetic data. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales in conjunction with theory, while realizing a path towards integrating band mapping data into materials science databases

    Finding Apparent Horizons in Dynamic 3D Numerical Spacetimes

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    We have developed a general method for finding apparent horizons in 3D numerical relativity. Instead of solving for the partial differential equation describing the location of the apparent horizons, we expand the closed 2D surfaces in terms of symmetric trace--free tensors and solve for the expansion coefficients using a minimization procedure. Our method is applied to a number of different spacetimes, including numerically constructed spacetimes containing highly distorted axisymmetric black holes in spherical coordinates, and 3D rotating, and colliding black holes in Cartesian coordinates.Comment: 19 pages, 13 figures, LaTex, to appear in Phys. Rev. D. Minor changes mad

    Ultrafast photocurrents at the surface of the three-dimensional topological insulator Bi2Se3\mathrm{Bi}_2\mathrm{Se}_3

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    Topological insulators constitute a new and fascinating class of matter with insulating bulk yet metallic surfaces that host highly mobile charge carriers with spin-momentum locking. Remarkably, the direction and magnitude of surface currents can be controlled with tailored light beams, but the underlying mechanisms are not yet well understood. To directly resolve the "birth" of such photocurrents we need to boost the time resolution to the scale of elementary scattering events (∌\sim 10 fs). Here, we excite and measure photocurrents in the three-dimensional model topological insulator Bi2Se3\mathrm{Bi}_2\mathrm{Se}_3 with a time resolution as short as 20 fs by sampling the concomitantly emitted broadband THz electromagnetic field from 1 to 40 THz. Remarkably, the ultrafast surface current response is dominated by a charge transfer along the Se-Bi bonds. In contrast, photon-helicity-dependent photocurrents are found to have orders of magnitude smaller magnitude than expected from generation scenarios based on asymmetric depopulation of the Dirac cone. Our findings are also of direct relevance for optoelectronic devices based on topological-insulator surface currents

    Synthetic 3D Pap smear nucleus generation

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    GĂłmez Aguilar, S. (2010). Synthetic 3D Pap smear nucleus generation. http://hdl.handle.net/10251/10215.Archivo delegad

    Applied Symmetry for Crystal Structure Prediction

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    This thesis presents an original open-source Python package called PyXtal (pronounced pi-crystal ) that generates random symmetric crystal structures for use in crystal structure prediction (CSP). The primary advantage of PyXtal over existing structure generation tools is its unique symmetrization method. For molecular structures, PyXtal uses an original algorithm to determine the compatibility of molecular point group symmetry with Wyckoff site symmetry. This allows the molecules in generated structures to occupy special Wyckoff positions without breaking the structure\u27s symmetry. This is a new feature which increases the space of search-able structures and in turn improves CSP performance. It is shown that using already-symmetric initial structures results in a higher probability of finding the lowest-energy structure. Ultimately, this lowers the computational time needed for CSP. Structures can be generated for any symmetry group of 0, 1, 2, or 3 dimensions of periodicity. Either atoms or rigid molecules may be used as building blocks. The generated structures can be optimized with VASP, LAMMPS, or other computational tools. Additional options are provided for the lattice and inter-atomic distance constraints. Results for carbon and silicon crystals, water ice crystals, and molybdenum clusters are presented as usage examples
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