Thickness control of Molecular Beam Epitaxy grown layers at the 0.01-0.1 monolayer level

Electron tunnelling through semiconductor tunnel barriers is exponentially sensitive to the thickness of the barrier layer, and in the most common system, the AlAs tunnel barrier in GaAs, a one monolayer variation in thickness results in a 300% variation in the tunnelling current for a fixed bias voltage. We use this degree of sensitivity to demonstrate that the level of control at 0.06 monolayer can be achieved in the growth by molecular beam epitaxy, and the geometrical variation of layer thickness across a wafer at the 0.01 monolayer level can be detected.

Noncontact measurement of charge carrier lifetime and mobility in GaN nanowires

The first noncontact photoconductivity measurements of gallium nitride nanowires (NWs) are presented, revealing a high crystallographic and optoelectronic quality achieved by use of catalyst-free molecular beam epitaxy. In comparison with bulk material, the NWs exhibit a long conductivity lifetime (>2 ns) and a high mobility (820 ± 120 cm 2/(V s)). This is due to the weak influence of surface traps with respect to other III-V semiconducting NWs and to the favorable crystalline structure of the NWs achieved via strain-relieved growth. © 2012 American Chemical Society.

Collapse of a composite beam made from ultra high molecular-weight polyethylene fibres

Hot-pressed laminates with a [0/90]48 lay-up, consisting of 83% by volume of ultra high molecular-weight polyethylene (UHMWPE) fibres, and 17% by volume of polyurethane (PU) matrix, were cut into cantilever beams and subjected to transverse end-loading. The collapse mechanisms were observed both visually and by X-ray scans. Short beams deform elastically and collapse plastically in longitudinal shear, with a shear strength comparable to that observed in double notch, interlaminar shear tests. In contrast, long cantilever beams deform in bending and collapse via a plastic hinge at the built-in end of the beam. The plastic hinge is formed by two wedge-shaped microbuckle zones that grow in size and in intensity with increasing hinge rotation. This new mode of microbuckling under macroscopic bending involves both elastic bending and shearing of the plies, and plastic shear of the interface between each ply. The double-wedge pattern contrasts with the more usual parallel-sided plastic microbuckle that occurs in uniaxial compression. Finite element simulations and analytical models give additional insight into the dominant material and geometric parameters that dictate the collapse response of the UHMWPE composite beam in bending. Detailed comparisons between the observed and predicted collapse responses are used in order to construct a constitutive model for laminated UHMWPE composites. © 2013 Elsevier Ltd.

An ngram-based statistical machine translation decoder

In this paper we describe MARIE, an Ngram-based statistical machine translation decoder. It is implemented using a beam search strategy, with distortion (or reordering) capabilities. The underlying translation model is based on an Ngram approach, extended to introduce reordering at the phrase level. The search graph structure is designed to perform very accurate comparisons, what allows for a high level of pruning, improving the decoder efficiency. We report several techniques for efficiently prune out the search space. The combinatory explosion of the search space derived from the search graph structure is reduced by limiting the number of reorderings a given translation is allowed to perform, and also the maximum distance a word (or a phrase) is allowed to be reordered. We finally report translation accuracy results on three different translation tasks.

Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.