Handling a very large data set for determination of surface tension of chemical compounds using Quantitative Structure–Property Relationship strategy

In this work, the Quantitative Structure–Property Relationship (QSPR) strategy is applied to represent/predict the surface tension of pure chemical compounds at (66.36–977.40) K temperature range. To propose a comprehensive, reliable, and predictive model, 18298 data belonging to experimental surface tension values of 1604 chemical compounds at different temperatures are studied. The Sequential Search mathematical method has been observed to be the only variable search method capable of selection of appropriate model parameters (molecular descriptors) regarding this large data set. To develop the final model, a three-layer Artificial Neural Network has been optimized using the Levenberg–Marquardt (LM) optimization strategy. Using this dedicated strategy, we obtain satisfactory results quantified by the following statistical parameters: absolute average deviations of the represented/predicted properties from existing experimental values: 3.8%, and squared correlation coefficient: 0.985.

► The largest surface tension data base (18298 data) has been applied to develop and test a QSPR model. ► The Sequential Search algorithm has been employed to select the appropriate model parameters. ► The results show that the obtained model is the most comprehensive one available in the literature.