摘要
A three-descriptor quantitative structure-property relationship （QSPR） model, based on the support vector machine （SVM） algorithm, was constructed to predict the glass transition temperatures （Tgs） ofpolyarylates with complex structures. A total of 50 polyarylates were randomly divided into three sets, viz., the training set （30 polymers）, validation set （10 polymers） and prediction set （10 polymers）. By adjusting various parameters by trial and error, the final optimum SVM model based on Austin Model 1 （AM1） calculation is a polynomial kernel with the parameters C of 100, ε of 1.00E-05 and d of 2. The root-mean-square （RMS） errors obtained from the training set, validation set and prediction set are 19.4, 12.8 and 15.5 K, respectively. Research results show that the proposed SVM model has better statistical quality than the previous models. Thus, applying the SVM algorithm to predict Tgs of polymers is feasible.
A three-descriptor quantitative structure-property relationship （QSPR） model, based on the support vector machine （SVM） algorithm, was constructed to predict the glass transition temperatures （Tgs） ofpolyarylates with complex structures. A total of 50 polyarylates were randomly divided into three sets, viz., the training set （30 polymers）, validation set （10 polymers） and prediction set （10 polymers）. By adjusting various parameters by trial and error, the final optimum SVM model based on Austin Model 1 （AM1） calculation is a polynomial kernel with the parameters C of 100, ε of 1.00E-05 and d of 2. The root-mean-square （RMS） errors obtained from the training set, validation set and prediction set are 19.4, 12.8 and 15.5 K, respectively. Research results show that the proposed SVM model has better statistical quality than the previous models. Thus, applying the SVM algorithm to predict Tgs of polymers is feasible.
基金
supported by the Open Project Program of Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education,China (No.10HJYH06)
