Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine.

As a novel type of learning machine method a support vector machine (SVM) was first used to develop a quantitative structure-property relationship (QSPR) model for the latest surface tension data of common diversity liquid compounds. Each compound was represented by structural descriptors, which were calculated from the molecular structure by the CODESSA program. The heuristic method (HM) was used to search the descriptor space, select the descriptors responsible for surface tension, and give the best linear regression model using the selected descriptors. Using the same descriptors, the non-linear regression model was built based on the support vector machine. Comparing the results of the two methods, the non-linear regression model gave a better prediction result than the heuristic method. Some insights into the factors that were likely to govern the surface tension of the diversity compounds could be gained by interpreting the molecular descriptors, which were selected by the heuristic model. This paper proposes a new effective way of researching interface chemistry, and can be very helpful to industry.