Abstract The glass transition temperature (Tg) is a fundamental characteristic of an amorphous polymer. A quantitative structure-property relationship (QSPR) based on error back-propagation artificial neural network (ANN) was constructed to predict Tgs of 107 polystyrenes. Stepwise multiple linear regression (MLR) analysis was adopted to select an optimal subset of molecular descriptors. The chain segments (or motion units) of polymer backbones with 20 carbons in length (10 repeating units) were used to calculate these molecular descriptors reflecting polymer structures. The relative optimal conditions of ANN were obtained by adjusting various network paramters by trial-and-error. Compared to the model already published in the literature, the optimal ANN model with [4-7-1] network structure in this paper is accurate and acceptable, although our model has more samples in the test set. The results demonstrate the feasibility and powerful ability of the chain segment structures as representative of polymers for developing Tg models of polystyrenes.

In this work we have performed a QSAR study of atmospheric reactions with hydroxyl radical, at the B3LYP level of theory with 6-31G(d) basis set. Molecular descriptors selected by applying multiple linear stepwise regression (MLR) analysis were used to predict the reaction rate constants (-logkOH) of OH radicals with organic compounds in the atmosphere, including 98 alkenes and 80 aromatic compounds. For setting our reactions, we have calculated 98 alkenes and 80 aromatic compounds. A four-descriptor MLR model (rms = 0.102 and R²= 0.938) for 98 alkenes was developed based on the number of R=CHX functional group counts, E HOMO parameter, and Fukui indices of the double-bonded C atoms. We found vinyl chemicals with conjugated double bonds and electron-donor substituents are the most reactive systems; while alkenes with multiple halogen substitutions are the least reactive molecules. Additionally, a three-descriptor MLR model (rms = 0.282 and R²= 0.910) was built to predict OH radical rate constants for 80 aromatic compounds, which was dominated by the E HOMO parameter, a topological descriptor for steric hindrance, and the most positive net atomic charge on hydrogen atoms. Aromatics with electron-donor and electron-acceptor groups, respectively, possess high and low degradation rates. The halogen aromatics are less reactive, especially for aromatics with multiple halogen substitutions. In comparison to existing models, the two models obtained in this paper show better statistical quality.

As constantes de velocidade da reação do ozônio com 95 alcenos (-logkO3) e do radical hidroxila (•OH) com 98 alcenos (-logkOH) na atmosfera foram previstas por modelos de relações quantitativas entre estrutura e atividade (QSAR). Cálculos usando a teoria do funcional da densidade (DFT) foram realizados para os respectivos alcenos no estado fundamental e para as estruturas do estado de transição para o processo de degradação na atmosfera. Técnicas de regressão linear múltipla (MLR) e de redes neurais de regressão generalizada (GRNN) foram utilizadas para desenvolver os modelos. O modelo GRNN de -logkO3 com base em três descritores e propagação ideal σ de 0,09 tem erro quadrático médio (rms) de 0,344; o modelo GRNN de -logkOH com quatro descritores e propagação ideal σ de 0,14 produz um erro rms de 0,097. Comparado com os modelos da literatura, os modelos GRNN neste artigo mostram estatísticas melhores. A importância dos descritores associados aos estados de transição na previsão de kO3 e kOH nos processos de degradação atmosférica foi demonstrada.

The reaction rate constants of ozone with 95 alkenes (-logkO3) and the hydroxyl radical (•OH) with 98 alkenes (-logkOH) in the atmosphere were predicted by quantitative structure-activity relationship (QSAR) models. Density functional theory (DFT) calculations were carried out on respective ground-state alkenes and transition-state structures of degradation processes in the atmosphere. Stepwise multiple linear regression (MLR) and general regression neural network (GRNN) techniques were used to develop the models. The GRNN model of -logkO3 based on three descriptors and the optimal spread σ of 0.09 has the mean root mean square (rms) error of 0.344; the GRNN model of -logkOH having four descriptors and the optimal spread σ of 0.14 produces the mean rms error of 0.097. Compared with literature models, the GRNN models in this article show better statistical characteristics. The importance of transition state descriptors in predicting kO3 and kOH of atmospheric degradation processes has been demonstrated.

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 C¹H3 - C²HR³• or •C¹H2 - C²H2R³ (formed from vinyl monomers C¹H²=C²HR³ + 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, ε = 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, ε = 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.

Recently, the support vector machine (SVM), as a novel type of learning machine, has been introduced to solve chemical problems. In this study, å- support vector regression (å-SVR) and v-support vector regression (v-SVR) were, respectively, used to construct quantitative structure-property relationship (QSPR) models of Q and e parameters in the Q-e scheme, which is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. The quantum chemical descriptors used to developed the SVR models were calculated from styrene and radicals with structures CH3CH2C¹H2-C²HR³· (C¹H2=C²HR³ + CH3CH2· - CH3CH2C¹H2-C²HR³·). The optimum å-SVR model of lnQ (C= 9, å =0.05 and ã =0.2) and the optimum v-SVR model of e (C=100, v = 0.5 and ã =0.4) produced low root mean square (rms) errors for prediction sets: 0.318 and 0.266, respectively. Thus, applying SVR to predict parameters Q and e is successful.