Determination of Solvent Dielectric Constant Using Artificial Neural Network -Analysing Spectral Changes of a Sensor Anion-

Abstract

Artificial neural networks (ANN) have widely been used in predicting physical properties of chemical materials. In this work, an ANN analysis is applied to determining dielectric constants of various pure solvents (i.e. water, chloroform, acetonitrile, methanol, formamide, etc) and aqueous mixed solvents (dioxane-water etc). A solvent dielectric constant is a physical property which is quite difficult to measure precisely. We have already reported that the circular dichroism spectrum of an optically active cobalt(III) complex is effected by solvents used. By using peak positions (nm) and CD intensities of the cobalt(III) complex as a sensor molecule in various solvents, we tried to predict solvent dielectric constants. Multilayer neural networks were chosen for pattern-recognition analysis. The architecture consists of input, output, and hidden layers, which are where data are processed through a back propagation training algorithm. Of the 14 spectra measured for pure solvents, 13 of these were used to train the data set (peak positions (nm) and CD intensities) obtained from each spectrum. When the remaining one unknown is processed, an absolute error of a few % was obtained for the output solvent dielectric constants.