Quantitative structure-property relationships and neural networks: correlation and prediction of physical properties of pure components and mixtures from molecular structure

With the molecular structure of a molecule at hand the solution of the Schr odinger equation would allow the prediction of any physical, chemical or bi ological property in stationary states of molecules. However, although much progress has been made, particularly with semi-empirical methods, the prac tical application of quantum theory to complex molecules remains a distant possibility. As an alternative approach, QSPR (quantitative structure-prope rty relationships) employ structural descriptors to develop correlations be tween the molecular structure and the physical property under investigation . High quality models have been developed for the normal boiling points of chlorosilanes, for the enthalpy of fusion of esters and for an equation of state mixture parameter for binary carbon dioxide-hydrocarbon systems using multilinear and nonlinear correlation techniques. The prediction capabilit y for properties of compounds not present in the training set proved to be excellent for all properties correlated in this study, mostly within the ac curacy of experimental measurements. (C) 1999 Elsevier Science B.V. All rig hts reserved.