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Prediction of the Q – e parameters from transition state structures
Author(s) -
Yu Xinliang,
Yu Ruqin
Publication year - 2013
Publication title -
polymer engineering and science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.503
H-Index - 111
eISSN - 1548-2634
pISSN - 0032-3888
DOI - 10.1002/pen.23470
Subject(s) - monomer , support vector machine , root mean square , mean squared error , state (computer science) , materials science , thermodynamics , work (physics) , correlation coefficient , mathematics , statistical physics , physics , computer science , algorithm , statistics , artificial intelligence , quantum mechanics , polymer , composite material
The Q – e scheme is remarkably useful in interpreting and predicting the reactivity of a monomer in free radical copolymerizations. In the present work, two support vector regression (SVR) models were developed to predict parameters Q and e in the Q – e scheme. Quantum chemical descriptors used to build SVR models were calculated, for the first time, from transition state species with structures C 1 H 3 —C 2 HR 3 • or •C 1 H 2 —C 2 H 2 R 3 , formed from vinyl monomer C 1 H 2 C 2 HR 3 + H•. The optimal ν‐SVR model of ln Q ( C = 130, ν = 0.2, and γ = 1.0) based on 70 monomers has the root mean square (rms) error of 0.336 and correlation coefficient ( R ) of 0.982. The optimal ε‐SVR model of e ( C = 1.2, γ = 3, and ε = 10 −2 ) produces rms = 0.259 and R = 0.963. Compared with previous models, the SVM models in this article have better predictive performance. Results of the study suggest that calculating quantum chemical descriptors from the transition state structures to predict parameters Q and e in the Q – e scheme is feasible. This investigation encourages the further application of transition state descriptors to other quantitative structure–activity relationships (QSARs). POLYM. ENG. SCI., 53:2151–2158, 2013. © 2013 Society of Plastics Engineers