Nature of the electronic band gap in lanthanide oxides

Accurate electronic structures of the technologically important lanthanide/rare-earth sesquioxides (Ln2O3, with Ln=La, ⋯,Lu) and CeO2 have been calculated using hybrid density functionals HSE03, HSE06, and screened exchange (sX-LDA). We find that these density functional methods describe the strongly correlated Ln f electrons as well as the recent G0W0@LDA+U results, generally yielding the correct band gaps and trends across the Ln period. For HSE, the band gap between O 2p states and lanthanide 5d states is nearly independent of the lanthanide, while the minimum gap varies as filled or empty Ln 4f states come into this gap. sX-LDA predicts the unoccupied 4f levels at higher energies, which leads to a better agreement with experiments for Sm2O 3, Eu2O3, and Yb2O3. © 2013 American Physical Society.

Flow field measurements of a series of turbulent premixed and stratified methane/air flames

This paper presents flow field measurements for the turbulent stratified burner introduced in two previous publications in which high resolution scalar measurements were made by Sweeney et al. [1,2] for model validation. The flow fields of the series of premixed and stratified methane/air flames are investigated under turbulent, globally lean conditions (φg=0.75). Velocity data acquired with laser Doppler anemometry (LDA) and particle image velocimetry (PIV) are presented and discussed. Pairwise 2-component LDA measurements provide profiles of axial velocity, radial velocity, tangential velocity and corresponding fluctuating velocities. The LDA measurements of axial and tangential velocities enable the swirl number to be evaluated and the degree of swirl characterized. Power spectral density and autocorrelation functions derived from the LDA data acquired at 10kHz are optimized to calculate the integral time scales. Flow patterns are obtained using a 2-component PIV system operated at 7Hz. Velocity profiles and spatial correlations derived from the PIV and LDA measurements are shown to be in very good agreement, thus offering 3D mapping of the velocities. A strong correlation was observed between the shape of the recirculation zones above the central bluff body and the effects of heat release, stoichiometry and swirl. Detailed analyses of the LDA data further demonstrate that the flow behavior changes significantly with the levels of swirl and stratification, which combines the contributions of dilatation, recirculation and swirl. Key turbulence parameters are derived from the total velocity components, combining axial, radial and tangential velocities. © 2013 The Combustion Institute.

A hybrid density functional view of native vacancies in gallium nitride.

We investigated the transition energy levels of the vacancy defects in gallium nitride by means of a hybrid density functional theory approach (DFT). We show that, in contrast to predictions from a recent study on the level of purely local DFT, the inclusion of screened exchange stabilizes the triply positive charge state of the nitrogen vacancy for Fermi energies close to the valence band. On the other hand, the defect levels associated with the negative charge states of the nitrogen vacancy hybridize with the conduction band and turn out to be energetically unfavorable, except for high n-doping. For the gallium vacancy, the increased magnetic splitting between up-spin and down-spin bands due to stronger exchange interactions in sX-LDA pushes the defect levels deeper into the band gap and significantly increases the associated charge transition levels. Based on these results, we propose the ϵ(0| - 1) transition level as an alternative candidate for the yellow luminescence in GaN.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.