Quantum molecular data analysis

Bjorsvik, H.R. and Priebe, H. (1995). Multivariate Data Analysis of Molecular Descriptors Estimated by Use of Semiempirical Quantum Chemistry Methods. Principal Properties for Synthetic Screening of 2-Chloromethyloxirane and Analogous bis-Alkylating C-3 Moieties. Acta Chem.Scand.,49,446-456. 

Bjorsvik, H.R. and Priebe, H. (1995) Multivariate data analysis of molecular descriptors estimated by use of semiempirical quantum chemistry methods. Principal properties for synthetic screening of 2-chloromethyloxirane and analogous bis-alkylating C-3 moieties. Acta Chem. Scand., 49, 446-456. 

On the instrumental side, most inelastic neutron scattering setups are capable of molecular spectroscopic experiments perhaps TOSCA, installed at the ISIS Pulsed Neutron and Muon Facility (UK), should be mentioned as it is dedicated to such studies (ISIS 2003). Data analysis most often includes quantum chemical calculations of the vibrational density of states, which are compared to experiment (see, e.g.. Line and Kearley 2000 Fernandez-Liencres et al. 2001). 

In recent years, the topological analysis of the three-dimensional scalar fields [87-95], such as electron density [55, 67, 92, 95-97], the Laplacian of the electron density [68, 92], the electron localization function (ELF) [94, 98], and molecular electrostatic potential, have been widely used to discern chemical structure and reactivity. This procedure, named quanmm chemical topology (QCT) [99] has been utilized for the study of chemical stmcture and reactivity [100-106]. Since its origins, the well-known approach of the atoms in molecules quantum theory (QTAIM), has evolved to be an invaluable tool for the chemical interpretation of quantum mechanical data, which relies on the properties of the electron density p(r) when atoms interact. Excellent reviews on QTAIM methods have been published elsewhere [69, 96, 107-109]. 

Vectors A series of scalars can be arranged in a column or in a row. Then, they are called a column or a row vector. If the elements of a column vector can be attributed to special characteristics, e.g., to compounds, then data analysis can be completed. The chemical structures of compounds can be characterized with different numbers called descriptors, variables, predictors, or factors. For example, toxicity data were measured for a series of aromatic phenols. Their toxicity can be arranged in a column arbitrarily Each row corresponds to a phenolic compound. A lot of descriptors can be calculated for each compound (e.g., molecular mass, van der Waals volume, polarity parameters, quantum chemical descriptors, etc.). After building a multivariate model (generally one variable cannot encode the toxicity properly) we will be able to predict toxicity values for phenolic compounds for which no toxicity has been measured yet. The above approach is generally called searching quantitative structure - activity relationships or simply QSAR approach. 

For the first time, the primary nitrone (formaldonitrone) generation and the comparative quantum chemical analysis of its relative stability by comparison with isomers (formaldoxime, nitrosomethane and oxaziridine) has been described (357). Both, experimental and theoretical data clearly show that the formal-donitrones, formed in the course of collision by electronic transfer, can hardly be molecularly isomerized into other [C,H3,N,0] molecules. Methods of quantum chemistry and molecular dynamics have made it possible to study the reactions of nitrone rearrangement into amides through the formation of oxaziridines (358). 

One advantage of this response of molecular fragment approach [24] to condensed Fukui functions is that Equations 18.21 through 18.24 are easily evaluated from the population analysis data that accompanies the output of most quantum chemistry calculations. 

The size of the atoms and the rigidity of the bonds, bond angles, torsions, etc. are determined empirically, that is, they are chosen to reproduce experimental data. Electrons are not part of the MM description, and as a result, several key chemical phenomena cannot be reproduced by this method. Nevertheless, MM methods are orders of magnitude cheaper from a computational point of view than quantum mechanical (QM) methods, and because of this, they have found a preferential position in a number of areas of computational chemistry, like conformational analysis of organic compounds or molecular dynamics. 

We examine the derivation of information about molecular structure and properties from analysis of pure rotational and vibration-rotational spectral data of diatomic molecular species on the basis of Dunham s algebraic formalism, making comparison with results from alternative approaches. According to an implementation of computational spectrometry, wave-mechanical calculations of molecular electronic structure and properties have already played an important role in spectral reduction through interaction of quantum chemistry and spectral analysis. 

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