Computers & chemistry最新文献

The generator coordinate Hartree–Fock method is used to generate universal basis sets (UBSs) of Gaussian- and Slater-type functions for low-lying excited states of some mono-positive and -negative ions. From our basis sets the total energies are calculated and compared with the corresponding results obtained with other UBSs and with fully-optimized basis sets of STFs.

Investigators using models to determine the phototoxic effects of sunlight on polycyclic aromatic hydrocarbons (PAHs) have invoked the excited states of the molecule as important in elucidating the mechanism of these reactions. Energies of actual excited states were calculated for ten PAHs by several ab initio methods. The main method used for these calculations was the Configuration Interaction approach, modeling excited states as combinations of single substitutions out of the Hartree–Fock ground state. These calculations correlate well with both experimentally measured singlet and triplet state energies and also previous HOMO–LUMO gap energies that approximate the singlet state energies. The excited state calculations then correlate well with general models of photo-induced toxicity based for the PAHs.

This paper presents the Vienna ab initio simulation package (VASP) data viewer, a desktop 3D visualization application for the analysis of valence electronic structure information derived from first-principles quantum-mechanical density functional calculations. This tool allows a scientist to directly view and manipulate the calculated charge density or electron localization function (ELF) from an electronic structure calculation, providing insight into the nature of chemical bonding. Particular attention was given to the design and implementation of the user interface (UI) for the data viewer. It provides for expert and novice usage, and both natural direct manipulation and precise numerical control. The data viewer has proven useful to chemical scientists for understanding the results of electronic structure calculations.

In this paper, the neural network method was applied to predict the content of protein secondary structure elements that was based on ‘pair-coupled amino acid composition’, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent-dataset. Both indicated good results obtained when using the neural network method to predict the contents of α-helix, β-sheet, parallel β-sheet strand, antiparallel β-sheet strand, β-bridge, 3₁₀-helix, π-helix, H-bonded turn, bend, and random coil.

A new software package, ‘kinfitsim’, for fitting and simulating kinetic data is presented. The main goals of the kinfitsim package are to obtain the best-fit parameters—rate constants, amplitudes and others—to a user specified chemical mechanism, plots of the calculated and experimental absorbance versus time, and a report to the user with the results. The kinfitsim package can be used in either chemical research or for educational purposes.

Models of lipid bilayer were extended and dipole structure of polar head in lipid molecules was included. As a result a wavy structure, resembling experimentally observed ‘ripple phase’, was obtained. The discussion on significance of interactions between dipoles that constitute polar part of the model membrane is presented. Assumptions of the model are closer to the real conditions and reflect the real phenomena much better. Dependence of the model system behaviour on dielectric permeability, ionic strength, and temperature was studied. An influence of reduced number of freedom degrees in the dipole system on the membrane properties was also considered. It was proved that if dielectric permeability of membrane polar part is significantly smaller than water dielectric permeability then the membrane model does not have to take into account changeability of dipole tilt towards membrane surface. This assumption becomes more significant for dielectric permeability ε approaching ε=80. Packing degree of hydrocarbon chains in hydrophobic part of the membrane is also responsible for the angle value between dipoles and the membrane surface. The model results are compared to experimental results obtained by means of fluorescence probe fluorescein-PE.

The multistep consecutive ECE–ECE reduction process $\text{A}\text{→}\text{e}\text{B}\text{→}\text{k}{}_{\mathrm{<mtext>f</mtext><mtext>1</mtext>}}\text{C}\text{→}\text{e}\text{D}\text{→}\text{e}\text{E}\text{→}\text{k}{}_{\mathrm{<mtext>f</mtext><mtext>2</mtext>}}\text{F}\text{→}\text{e}\text{G}$ has been compared with reduction in multicomponent system $\text{A}\text{→}\text{e}\text{B}\text{,}\text{C}\text{→}\text{e}\text{D}\text{,}\text{D}\text{→}\text{e}\text{E}\text{,}\text{F}\text{→}\text{e}\text{G}$. A simple method of transformation has been devised to disclose the subtle structure of the complex cyclic voltammetry (CV) responses and illustrated by the ECE–ECE process modeled earlier. The method can be applied to any multi-electron CV experimental curve for which a numerical modeling has been done. Electroreduction processes similar to those considered here are often met in practice. An attempt of unification of consecutive electroreduction and electroreduction of multicomponent system has been made. Interrelation between research and analytical voltammetry aspects of the problem is also discussed.

A mathematica® package, ‘condu.m’, has been developed to find the polynomial in concentration and temperature which best fits conductimetric data of the type (κ, c, T) or (κ, c₁, c₂, T) of electrolyte solutions (κ: specific conductivity; c_i: concentration of component i; T: temperature). In addition, an interface, ‘tkondu’, has been written in the TCL/Tk language to facilitate the use of condu.m by an operator not familiarised with mathematica®. All this software is available on line (UPV/EHU, 2001). ‘condu.m’ has been programmed to: (i) select the optimum grade in c₁ and/or c₂; (ii) compare models with linear or quadratic terms in temperature; (iii) calculate the set of adjustable parameters which best fits data; (iv) simplify the model by elimination of ‘a priori’ included adjustable parameters which after the regression analysis result in low statistical significance; (v) facilitate the location of outlier data by graphical analysis of the residuals; and (vi) provide quantitative statistical information on the quality of the fit, allowing a critical comparison among different models. Due to the multiple options offered the software allows testing different conductivity models in a short time, even if a large set of conductivity data is being considered simultaneously. Then, the user can choose the best model making use of the graphical and statistical information provided in the output file. Although the program has been initially designed to treat conductimetric data, it can be also applied for processing data with similar structure, e.g. (P, c, T) or (P, c₁, c₂, T), being P any appropriate transport, physical or thermodynamic property.