Ab initio density functional calculations of the structural and electronic properties of V(2)O(5) bulk and its low-index surfaces are presented. For the bulk oxide and the (010) surface (the natural cleavage plane) a good agreement with experiment and with earlier ab initio calculations is found. For the first time, the investigations are extended to other low-index surfaces: (001) and (100). On both surfaces, termination conserving a bulk-like stoichiometry is preferred, but-in contrast to the (010) surface-a strong structural relaxation takes place. Relaxation reduces the surface energy from 1.16 to 0.48 J m(-2) for the (001) and from 0.61 to 0.55 J m(-2) for the (100) surface. Although the relaxed surface energies are still one order of magnitude higher than calculated for the (010) surface (0.047 J m(-2)), the Wulff construction demonstrates that (001) and (100) surfaces contribute about 15% of the total surface area of a V(2)O(5) crystallite, indicating a non-negligible role in the catalytic activity of V(2)O(5).

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Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure-activity relationship (QSAR) approaches.

We used static DFT calculations to analyze, in detail, the intramolecular hydrogen bonds formed in low-molecular-weight polyethylene glycol (PEG) with two to five repeat subunits. Both red-shifted O-H⋅⋅⋅O and blue-shifting C-H⋅⋅⋅O hydrogen bonds, which control the structural flexibility of PEG, were detected. To estimate the strength of these hydrogen bonds, the quantum theory of atoms in molecules was used. Car-Parrinello molecular dynamics simulations were used to mimic the structural rearrangements and hydrogen-bond breaking/formation in the PEG molecule at 300 K. The time evolution of the H⋅⋅⋅O bond length and valence angles of the formed hydrogen bonds were fully analyzed. The characteristic hydrogen-bonding patterns of low-molecular-weight PEG were described with an estimation of their lifetime. The theoretical results obtained, in particular the presence of weak C-H⋅⋅⋅O hydrogen bonds, could serve as an explanation of the PEG structural stability in the experimental investigation.