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首页> 外文期刊>Journal of Applied Polymer Science >A Simple Three-Descriptor Model for the Prediction of the Glass-Transition Temperatures of Vinyl Polymers
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A Simple Three-Descriptor Model for the Prediction of the Glass-Transition Temperatures of Vinyl Polymers

机译：预测乙烯基聚合物玻璃化转变温度的简单三描述模型

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An artificial neural network (ANN) implementing a back-propagation algorithm was applied for the prediction of the glass-transition temperature (T-g) values of 84 polvacrylates and 21 polyvinyls. The experimental T-g data of the polymers were divided into a training set (50 polyacrylates) and a testing set (34 polyacrylates and 21 polyvinyls). Three molecule descriptors (mean atomic van der Waals volume, bond information content, and three-dimensional molecule representation of structures based on electron diffraction descriptor for signal 13/weighted by atomic masses, Mor13m) were used as input parameters of the neural network. Simulated with the optimum back-propagation ANN 3-[3-2]-1, the root mean square (rms) error for the testing set was 17.7 K, and the correlation coefficient was 0.942, which were accurate in comparison with existing models. The ANN model could be used not only to reveal the quantitative relation between T-g and the molecular structure but also to predict the T-g values of the polyacrylates and polyvinyls.
机译：实施反向传播算法的人工神经网络（ANN）被用于预测84种聚丙烯酸丙烯酸酯和21种聚乙烯醇的玻璃化转变温度（T-g）值。将聚合物的实验T-g数据分为训练组（50个聚丙烯酸酯）和测试组（34个聚丙烯酸酯和21个聚乙烯基）。将三个分子描述符（平均原子范德华体积，键信息含量以及基于信号13 /被原子质量加权的Mor13m的电子衍射描述符的结构的三维分子表示）用作神经网络的输入参数。用最佳反向传播ANN 3- [3-2] -1模拟，测试集的均方根（rms）误差为17.7 K，相关系数为0.942，与现有模型相比是准确的。 ANN模型不仅可以用来揭示T-g与分子结构之间的定量关系，而且可以预测聚丙烯酸酯和聚乙烯基的T-g值。

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