Computational chemistry quantitative correlations

Physical properly estimation methods may be classified into six general areas (1) theory and empirical extension of theory, (2) corresponding states, (3) group contributions, (4) computational chemistry, (5) empirical and quantitative structure property relations (QSPR) correlations, and (6) molecular simulation. A quick overview of each class is given below to provide context for the methods and to define the general assumptions, accuracies, and limitations inherent in each. 

Empirical QSPR Correlations In quantitative structure property relationship (QSPR) methods, physical properties are correlated with molecular descriptors that characterize the molecular and electronic structure of the molecule. Large amounts of experimental data are used to statistically determine the most significant descriptors to be used in the correlation and their contributions. The resultant correlations are simple to apply if the descriptors are available. Descriptors must generally be generated by the user with computational chemistry software, although the DIPPR 801 database now contains a table of molecular descriptors for most of the compounds in it. QSPR methods are often very accurate for specific families of compounds for which the correlation was developed, but extrapolation problems are even more of an issue than with GC methods. 

To successfully correlate thermodynamic or kinetic parameters with stereo-electronic effects, there must be a clean separation of the electronic from the steric effects (Eq. 1). Since there is no necessary reason for an experimentally based steric parameter to be free of electronic effects, workers have turned to applications of computational chemistry to achieve a quantitative measure of pure steric influence of a ligand. 

In short, each reaction family could be described with a maximiun of three parameters (A, Eo, a). Procurement of a rate constant from these parameters required only an estimate of the enthalpy change of reaction for each elementary step. In principle, this enthalpy change of reaction amoimted to the simple calculation of the difference between the heats of formation of the products and reactants. However, since many model species, particularly the ionic intermediates and olefins, were without experimental values, a computational chemistry package, MOPAC, ° was used to estimate the heat of formations on the fly . Ihe organization of the rate constants into quantitative structure-reactivity correlations (QSRC) reduced the number of model parameters greatly Ifom O(IO ) to 0(10). 

The classical treatment for the quantitative determination of the steric effects operative in molecules was developed by Westheimer. Steric effects were considered as the sum of various independent strain producing mechanisms (bond strain, angle strain, torsional strain, non-bonded interaction strain). Westheimer s assumptions proved to be the fundamental basis for the BIGSTRN program as well as for all subsequent molecular mechanics treatments of neutral hydrocarbons and carbocations. Reactivities ranging over 10 ° could be correlated by the strain differences between cation and the neutral precursor. Gleicher and Schleyer s work was a historical breakthrough in the development of molecular mechanics and provided the basis for the predictions of rate constants of solvolysis reactions. For the first time chemical reactions could reliably be predicted by the means of computational chemistry. 

The BOVB method does not of course aim to compete with the standard ab initio methods. BOVB has its specific domain. It serves as an interface between the quantitative rigor of today s capabilities and the traditional qualitative matrix of concepts of chemistry. As such, it has been mainly devised as a tool for computing diabatic states, with applications to chemical dynamics, chemical reactivity with the VB correlation diagrams, photochemistry, resonance concepts in organic chemistry, reaction mechanisms, and more generally all cases where a valence bond reading of the wave function or the properties of one particular VB structure are desirable in order to understand better the nature of an electronic state. The method has passed its first tests of credibility and is now facing a wide field of future applications. 

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