Evaluations of thermal decomposition properties for optically active polymers based on support vector machine

The use of quantitative structure-property relationships is proposed for the calculation of temperature of 10 % mass loss (T ) for a number of 50 optically active polymers. The descriptors involved in these models were calculated from the structures of the repeating units. The important descriptors were selected applying genetic algorithm-partial least squares (GA-PLS) technique. A PLS method was used to select the best descriptors and the selected descriptors were used as inputs for support vector machine (SVM) model. The root mean square errors for the SVM calculated T of training and prediction sets are 9.842 and 10.384, respectively, which are smaller than those obtained by PLS model (27.970 and 34.416, respectively). The results obtained showed the ability of the developed SVM to predict T of various chiral polymers. Also results revealed the superiority of the SVM over the PLS model. © 2014 Akadémiai Kiadó, Budapest, Hungary.