PMID: 11911705Mar 26, 2002Paper

A general QSPR treatment for dielectric constants of organic compounds

Journal of Chemical Information and Computer Sciences
Sulev Sild, Mati Karelson

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%.

References

Feb 8, 2000·Journal of Chemical Information and Computer Sciences·A R KatritzkyM Karelson

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Citations

Jan 6, 2010·Journal of Molecular Modeling·Guillaume FayetCarlo Adamo
Sep 5, 2003·Advanced Drug Delivery Reviews·Jyrki Taskinen, Jouko Yliruusi
Feb 26, 2016·SAR and QSAR in Environmental Research·S SoltanpourM Taghi Baharifard
May 16, 2019·Physical Chemistry Chemical Physics : PCCP·Rémi Bouteloup, Didier Mathieu
Jun 25, 2019·Physical Chemistry Chemical Physics : PCCP·Théophile Gaudin, Haibo Ma
Sep 26, 2006·Journal of Chemical Information and Modeling·Mati KarelsonAlan R Katritzky
Apr 9, 2013·Journal of Chemical Theory and Computation·Gustavo A OrozcoAllan D Mackie

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