Evaluations of thermal decomposition properties for optically active polymers based on support vector machine, By Mehdi Hatami

Research

Title	Evaluations of thermal decomposition properties for optically active polymers based on support vector machine
Type	Article
Keywords	Quantitative structure–property relationship Thermal decomposition temperature Support vector machine Partial least squares Genetic algorithm
Researchers	Shadpour Mallakpour، Mehdi Hatami، Sajad Khooshechin، Hassan Golmohammadi

Abstract

The use of quantitative structure–property relationships is proposed for the calculation of temperature of 10 % mass loss (T10) 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 T10 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 T10 of various chiral polymers. Also results revealed the superiority of the SVM over the PLS model.