A General QSPR Treatment for Dielectric Constants of Organic Compounds

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Abstract

Multilinear regression and neural network methods have been used to develop QSPR models for the prediction of the dielectric constant (epsilon) and Kirkwood function (epsilon - 1)/(2epsilon + 1) of organic liquids. Both methods can provide acceptable models for the prediction of these properties. The QSPR models developed from the training set of 155 diverse compounds use theoretical molecular descriptors encoding electronic properties of the molecule and the intermolecular interaction between molecules. The QSPR models for the Kirkwood function appear to be more reliable than the models for the dielectric constant. The average prediction error of the best model for the dielectric constant is 27.0%. The average prediction error of the best model for the Kirkwood function is 4.1%.

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