37,966 research outputs found
A machine learning route between band mapping and band structure
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
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
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Ultrafast photocurrents at the surface of the three-dimensional topological insulator
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 ( 10 fs). Here, we excite and measure photocurrents in
the three-dimensional model topological insulator
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
GĂłmez Aguilar, S. (2010). Synthetic 3D Pap smear nucleus generation. http://hdl.handle.net/10251/10215.Archivo delegad
Applied Symmetry for Crystal Structure Prediction
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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