An efficient coarse-grained approach for the electron transport through large molecular systems under dephasing environment

Dephasing effects in electron transport in molecular systems connected between contacts average out the quantum characteristics of the system, forming a bridge to the classical behavior as the size of the system increases. For the evaluation of the conductance of the molecular systems which have sizes within this boundary domain, it is necessary to include these dephasing effects. These effects can be calculated by using the D’Amato-Pastawski model. However, this method is computationally demanding for large molecular systems since transmission functions for all pairs of atomic orbitals need to be calculated. To overcome this difficulty, we develop an efficient coarse-grained model for the calculation of conductance of molecular junctions including decoherence. By analyzing the relationship between chemical potential and inter-molecular coupling, we find that the chemical potential drops stepwise in the systems with weaker inter-unit coupling. Using this property, an efficient coarse-grained algorithm which can reduce computational costs considerably without losing the accuracy is derived and applied to one-dimensional organic systems as a demonstration. This model can be used for the study of the orientation dependence of conductivity in various phases (amorphous, crystals, and polymers) of large molecular systems such as organic semiconducting materials.     

The first three anisotropy constants of nickel

The technique of ferromagnetic resonance at 23 GHz has been used to determine the first three anisotropy constants of pure Ni down to 4.2K. A temperature and orientation dependent linewidth has also been observed.     

Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Do columnar defects produce bulk pinning?

From magneto-optical imaging performed on heavy-ion-irradiated YBa(2)Cu(3)O(7-delta) single crystals, it is found that at fields and temperatures where strong single vortex pinning by individual irradiation-induced amorphous columnar defects is to be expected, vortex motion is limited by the nucleation of vortex kinks at the specimen surface. In the material bulk, vortex motion occurs through (easy) kink sliding. Depinning in the bulk determines the screening current only at fields comparable to or larger than the matching field, at which the majority of vortices is not trapped by an ion track.