Prediction of electric conductivity for ionic liquids by two chemometrics methods

In recent years, the study of properties of ionic liquids (ILs) and their structures has developed to a great extent. Among the common physicochemical properties of pure ILs, electric conductivity (EC) is of crucial importance for both practical and fundamental levels. In order to develop effective models for predicting EC value of various ILs, the relationship between the structural descriptors and the EC of thirty-five ionic liquids at different temperatures has been investigated by multi linear regression (MLR) and back propagation artificial neural network (ANN), respectively. As a result, a three layer ANN with four variables selected by MLR model as input node was set up successfully. The descriptors selected by MLR were suitable and significant to be the input nodes of the ANN model in this study. And the calculated ionic conductivities by ANN model with high correlation coefficient and low root mean squared error were quantitative in good agreement with the experimental values, and it was proved better than the MLR model