Correlating dynamical mechanical properties with temperature and clay composition of polymer-clay nanocomposites
Correlating dynamical mechanical properties with temperature and clay composition of polymer-clay nanocomposites
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DOI：
10.1016/j.commatsci.2008.09.027
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发表时间：
2009-04-01
影响因子：
3.3
通讯作者：
Choi, Tae-Sun
中科院分区：
文献类型：
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作者：
Khan, Asifullah;Shamsi, Mohtashim H.;Choi, Tae-Sun
我们提出了先进的非线性回归模型的聚合物-粘土纳米复合材料（PCN），使用机器学习技术，如支持向量回归（SVR）和人工神经网络（ANN）的发展。开发的回归模型与温度和粘土组成的PCN的动态力学性能。首先将输入的关于自变量的特征空间转换到高维空间进行非线性回归。研究表明，温度和粘土组成对力学性能的影响是一种非线性现象，多元线性回归（MLR）不能很好地模拟这种非线性现象，而SVR和ANN的性能优于MLR。新样本的SVR平均相对误差为0.0648，而ANN和MLR的平均相对误差分别为0.0701和7.5909。SVR的良好的泛化能力代表了一个可行的定量结构-性质关系（QSPR）模型，该数据集在温度和粘土组成。QSPR模型的这种更好的泛化性能对于应用化学和材料科学的实际情况至关重要。所提出的预测模型可以非常有效地减少大量的实验室测试，以开发所需的机械性能的PCN。(C)2008 Elsevier B. V.保留所有权利。
We propose the development of advanced nonlinear regression models for polymer-clay nanocomposites (PCN) using machine learning techniques such as support vector regression (SVR) and artificial neural networks (ANN). The developed regression models correlate the dynamical mechanical properties of PCN with temperature and clay composition. The input feature space regarding the independent variables is first transformed into high dimensional space for Carrying Out nonlinear regression. Our investigation shows that the dependence of mechanical properties on temperature and clay composition is a nonlinear phenomenon and that Multiple linear regression (MLR) is unable to model it. It has been observed that SVR and ANN exhibits better performance when compared with MLR. Average relative error of SVR on the novel samples is 0.0648, while it is 0.0701 and 7.5909 for ANN and MLR, respectively. The good generalization capability of SVR represents a viable quantitative structure-property relationship (QSPR) model for this dataset across both temperature and clay composition. This better generalization property of a QSPR model is critical concerning practical situations in applied chemistry and materials science. The proposed prediction models could be highly effective in reducing multitude lab testing for developing PCN of desired mechanical properties. (C) 2008 Elsevier B.V. All rights reserved.