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Polymer Science Series B

, 53:528 | Cite as

A support vector machine model for the prediction of monomer reactivity ratios

Theory

Abstract

To predict monomer reactivity ratios in radical copolymerization of monomers M1 (C1H2=C2XY) with M2 (styrene), a support vector machine model was developed. After 16 quantum chemical descriptors were calculated by the density functional theory at B3LYP level of theory with 6–31G(d) basis set, the genetic algorithm method, together with multiple linear regression analysis, was used to select the best combinations of the variables. The optimal SVM model with four descriptors (\(q_{AC^1 }\), \(Q_{AC^2 }\), μ and E LUMO) was obtained with the Gaussian radical basis kernel (C = 8000, ɛ = 0.001 and γ = 0.01). The root-mean-square errors for training set, validation set and test set are 0.125, 0.123 and 0.188, respectively, which are more accurate than the existing artificial neural network model. Therefore, it is reasonable to predict monomer reactivity ratios with the support vector machine method.

Keywords

Support Vector Machine Artificial Neural Network Model Polymer Science Series Support Vector Machine Model Radical Copolymerization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Pleiades Publishing, Ltd. 2011

Authors and Affiliations

  1. 1.College of Chemistry and Chemical EngineeringHunan Institute of EngineeringXiangtan, HunanChina
  2. 2.Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education, College of ChemistryXiangtan UniversityXiangtan, HunanChina

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