Prediction of monomer reactivity in radical copolymerizations from transition state quantum chemical descriptors
| Autor: | Jiyong Deng, Shihua Zhang, Xinliang Yu, Zhengde Tan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Quantitative structure–activity relationship
radical copolymerizations Chemistry Organic Chemistry Thermodynamics State (functional analysis) transition state lcsh:Chemical technology Support vector machine quantum chemistry Genetic algorithm Computational chemistry Kernel (statistics) Radial basis function kernel structure-activity relations Chemical Engineering (miscellaneous) Density functional theory Reactivity (chemistry) support vector machine lcsh:TP1-1185 Basis set |
| Zdroj: | Polímeros, Vol 23, Iss 4, Pp 477-483 (2013) Polímeros, Volume: 23, Issue: 4, Pages: 477-483, Published: 20 AUG 2013 Polímeros v.23 n.4 2013 Polímeros (São Carlos. Online) Associação Brasileira de Polímeros (ABPol) instacron:ABPO |
| ISSN: | 1678-5169 |
| Popis: | In comparison with the Q-e scheme, the Revised Patterns Scheme: the U, V Version (the U-V scheme) has greatly improved both its accessibility and its accuracy in interpreting and predicting the reactivity of a monomer in free-radical copolymerizations. Quantitative structure-activity relationship (QSAR) models were developed to predict the reactivity parameters u and v of the U-V scheme, by applying genetic algorithm (GA) and support vector machine (SVM) techniques. Quantum chemical descriptors used for QSAR models were calculated from transition state species with structures C 1 H 3 -C 2 HR 3 • orC 1 H 2 -C 2 H 2 R 3 (formed from vinyl monomers C 1 H 2 =C 2 HR 3 + H•), using density functional theory (DFT), at the UB3LYP level of theory with 6-31G(d) basis set. The optimum support vector regression (SVR) model of the reactivity parameter u based on Gaussian radial basis function (RBF) kernel (C = 10, e = 10 -5 and γ = 1.0) produced root-mean-square (rms) errors for the training, validation and prediction sets being 0.220, 0.326 and 0.345, respectively. The optimal SVR model for v with the RBF kernel (C = 20, e = 10 -4 and γ = 1.2) produced rms errors for the training set of 0.123, the validation set of 0.206 and the prediction set of 0.238. The feasibility of applying the transition state quantum chemical descriptors to develop SVM models for reactivity parameters u and v in the U-V scheme has been demonstrated. |
| Databáze: | OpenAIRE |
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