Comparison between classical potentials and ab initio for silicon under large shear

The homogeneous shear of the {111} planes along the <110> direction of bulk silicon has been investigated using ab initio techniques, to better understand the strain properties of both shuffle and glide set planes. Similar calculations have been done with three empirical potentials, Stillinger-Weber, Tersoff and EDIP, in order to find the one giving the best results under large shear strains. The generalized stacking fault energies have also been calculated with these potentials to complement this study. It turns out that the Stillinger-Weber potential better reproduces the ab initio results, for the smoothness and the amplitude of the energy variation as well as the localization of shear in the shuffle set

Dislocation formation from a surface step in semiconductors: an ab initio study

The role of a simple surface defect, such as a step, for relaxing the stress applied to a semiconductor, has been investigated by means of large scale first principles calculations. Our results indicate that the step is the privileged site for initiating plasticity, with the formation and glide of 60$^\circ$ dislocations for both tensile and compressive deformations. We have also examined the effect of surface and step termination on the plastic mechanisms

A Comprehensive Spectroscopic Analysis of DB White Dwarfs

We present a detailed analysis of 108 helium-line (DB) white dwarfs based on model atmosphere fits to high signal-to-noise optical spectroscopy. We derive a mean mass of 0.67 Mo for our sample, with a dispersion of only 0.09 Mo. White dwarfs also showing hydrogen lines, the DBA stars, comprise 44% of our sample, and their mass distribution appears similar to that of DB stars. As in our previous investigation, we find no evidence for the existence of low-mass (M < 0.5 Mo) DB white dwarfs. We derive a luminosity function based on a subset of DB white dwarfs identified in the Palomar-Green survey. We show that 20% of all white dwarfs in the temperature range of interest are DB stars, although the fraction drops to half this value above Teff ~ 20,000 K. We also show that the persistence of DB stars with no hydrogen features at low temperatures is difficult to reconcile with a scenario involving accretion from the interstellar medium, often invoked to account for the observed hydrogen abundances in DBA stars. We present evidence for the existence of two different evolutionary channels that produce DB white dwarfs: the standard model where DA stars are transformed into DB stars through the convective dilution of a thin hydrogen layer, and a second channel where DB stars retain a helium-atmosphere throughout their evolution. We finally demonstrate that the instability strip of pulsating V777 Her white dwarfs contains no nonvariables, if the hydrogen content of these stars is properly accounted for.Comment: 74 pages including 30 figures, accepted for publication in the Astrophysical Journa

SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes

The spatio-temporal interpolation of large geophysical datasets has historically been adressed by Optimal Interpolation (OI) and more sophisticated model-based or data-driven DA techniques. In the last ten years, the link established between Stochastic Partial Differential Equations (SPDE) and Gaussian Markov Random Fields (GMRF) opened a new way of handling both large datasets and physically-induced covariance matrix in Optimal Interpolation. Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework. The reconstruction task is seen as a joint learning problem of the prior involved in the variational inner cost and the gradient-based minimization of the latter: both prior models and solvers are stated as neural networks with automatic differentiation which can be trained by minimizing a loss function, typically stated as the mean squared error between some ground truth and the reconstruction. In this work, we draw from the SPDE-based Gaussian Processes to estimate complex prior models able to handle non-stationary covariances in both space and time and provide a stochastic framework for interpretability and uncertainty quantification. Our neural variational scheme is modified to embed an augmented state formulation with both state and SPDE parametrization to estimate. Instead of a neural prior, we use a stochastic PDE as surrogate model along the data assimilation window. The training involves a loss function for both reconstruction task and SPDE prior model, where the likelihood of the SPDE parameters given the true states is involved in the training. Because the prior is stochastic, we can easily draw samples in the prior distribution before conditioning to provide a flexible way to estimate the posterior distribution based on thousands of members

Crystal Structure and Infrared Spectra of Anhydrous (Adeninato)methylmercury(II)

The title compound, C6H7HgN5, belongs to the orthorhombic space group Pbcn, a == 14.658(8), b == 8.407(5), c == 13.006(9) A, Deal = 2.898 g cm-3, Z == 8 molecules per cell. The structure was refinedon 1132 unique nonzero reflections to R == 0.026. The crystal containsthe same type of 1 : 1 monomeric molecule as the monohydrat in which the CH3Hg group is bonded to the N(9) position of deprotonated adenine. These molecules are associated into infinite ribbons by means of two pairs of complementary N-H ... N bonds involvingboth amino protons and the lone pairs on N(l) and N(7) of each molecule. The infrared spectra of this material and of the monohydrate differ almost exclusively in the regions related to NH2 vibrations. This simply results from differences in hydrogen- bonding patterns and no direct metal-amino interaction is taking place in any case. The spectral changes already identified as characteristicof N(9)-complexation for the monohydrate are also observed for the anhydrous compou nd