Correlation of structure and dielectric properties of silver selenomolybdate glasses

Structure and dielectric properties of the glasses of compositions yAg2O-(1-y)(xSeO2-(1-x)MoO3) with varying modifier oxide and glass formers ratio have been reported in this paper. Fourier transform infrared (FTIR) spectroscopy has been employed to investigate the effect of SeO2 content on the glass network structure. The existence of different characteristic absorption bands corresponding to the vibration of SeO32− anions, isolated MoO6 units and crystalline molybdate octahedra, has been ascertained from FTIR spectra. It has been observed that the modification of the glass network structure occurs with change of SeO2 content, which reveals the dual role of SeO2 as a network modifier and a network former depending on composition. The dielectric constant as well as dielectric strength increases gradually with the increase of SeO2 content for low modifier oxide content (y), whereas they show a maximum for intermediate and highly modified glasses. The variation of the dielectric parameters correlates directly to the relative proportion of vibration mode of SeO32− ions, which is observed to vary in a similar fashion to dielectric parameters and is, thus in turn, related to the dual behavior of SeO2 as a modifier and a former depending on composition

Transport properties of silver selenomolybdate glassy ionic conductors

Transport properties of silver selenomolybdate glassy ionic conductors have been reported for wide composition and temperature ranges. It has been observed that the transport properties of these glasses depend strongly on the modifier content as well as on the glass formers ratio. A direct correlation between the ion transport and the modification of the glass structure has been predicted. Transport properties of these glasses are also strongly influenced by the existence of dual character of SeO2 as a glass former and a glass modifier. Structural models for different compositions have also been proposed

Denoising Autoencoders for fast Combinatorial Black Box Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.Comment: corrected typos and small inconsistencie