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QSAR models of reaction rate constants of alkenes with ozone and hydroxyl radical

机译:烯烃与臭氧和羟基自由基反应速率常数的QSAR模型

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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.
机译:通过定量构效关系(QSAR)模型预测了臭氧在大气中与95个烯烃(-logkO3)和羟基自由基(•OH)与98个烯烃(-logkOH)的反应速率常数。对大气中降解过程的各个基态烯烃和过渡态结构进行了密度泛函理论(DFT)计算。使用逐步多元线性回归(MLR)和通用回归神经网络(GRNN)技术开发模型。 -logkO3的GRNN模型基于三个描述符,最佳扩展σ为0.09,均方根(rms)误差为0.344; -logkOH的GRNN模型具有四个描述符,最佳扩展σ为0.14,产生的均方根误差为0.097。与文献模型相比,本文的GRNN模型具有更好的统计特性。已经证明了过渡态描述符在预测大气退化过程的kO3和kOH中的重要性。

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