Prediction of surface tension, viscosity, and thermal conductivity for common organic solvents using quantitative structure-property relationships

Predictive models for the surface tension, viscosity, and thermal conductivity of 213 common organic solvents are reported. The models are derived from numerical descriptors which encode information about the topology, geometry, and electronics of each compound in the data set. Multiple linear regression and computational neural networks are used to train and evaluate models based on statistical indices and overall root-mean-square error. Eight-descriptor models were developed for both surface tension and viscosity, while a nine-descriptor model was developed for thermal conductivity. In addition, a single nine-descriptor model was developed for prediction of all three properties. The results of this study compare favorably to previously reported prediction methods for these three properties.