Identifying interphase properties in polymer nanocomposites using adaptive optimization
Identifying interphase properties in polymer nanocomposites using adaptive optimization
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DOI：
10.1016/j.compscitech.2018.04.017
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发表时间：
2018-07
影响因子：
9.1
通讯作者：
Yixing Wang;Yichi Zhang;He Zhao;Xiaolin Li;Yanhui Huang;L. Schadler;Wei Chen-;L. Brinson
中科院分区：
文献类型：
--
作者：
Yixing Wang;Yichi Zhang;He Zhao;Xiaolin Li;Yanhui Huang;L. Schadler;Wei Chen-;L. Brinson
为了预测纳米复合材料的性能，计算模型已经表明，界面行为可以用修改后的（移动）频率的矩阵属性表示。对于给定的样本数据集所需的偏移量可以通过在来自计算模型的预测曲线和实验数据之间通过试错法实现最佳拟合来确定。然而，由于实验数据的复杂性和昂贵的计算成本，手动过程来解决这个反问题是不切实际的处理许多实验数据集。这一困难阻碍了纳米复合材料界面背后的基本原理的研究。在这项工作中，我们提出了一种自适应优化方法，加速搜索聚合物纳米复合材料数据集的界面性质，通过求解逆问题，使用全局优化。我们的目标是最大限度地减少预测的纳米复合材料的整体性能与实验数据之间的差异。高斯过程（GP）模型被建立作为目标函数的替代品，并量化了预测的不确定性。自适应采样策略被应用于通过基于预期改进函数迭代地选择下一个采样点来有效地导航复杂搜索空间。代理模型和最优解不断演化，直到达到期望的目标。该方法在纳米复合材料的介电和粘弹性能模拟上进行了测试。我们的工作提供了深入了解使用自适应优化识别聚合物纳米复合材料的界面性质，并展示了数据驱动方法的潜力，以实现更深入地了解界面性质及其起源。
To predict the properties of nanocomposites, computational models have demonstrated that the interphase behavior can be expressed using the matrix properties with a modified (shifted) frequency. The amount of the shift necessary for a given sample data set can be determined by achieving the best fit between the predicted curve from a computation model and the experimental data through trial-and-error. However, with the complexity of experimental data and expensive computational costs, a manual process to solve this inverse problem is impractical to handle many experimental data sets. This difficulty hinders investigation of the underlying principles behind nanocomposite interphase. In this work, we present an adaptive optimization approach that accelerates the search for interphase properties in polymer nanocomposite data sets by solving the inverse problem using global optimization. The objective is to minimize the difference between the predicted bulk property of a nanocomposite with that from the experiment data. A Gaussian Process (GP) model is built as a surrogate of the objective function with quantification of prediction uncertainty. An adaptive sampling strategy is applied to effectively navigate the complex search space by iteratively selecting the next sampling point based on an expected improvement function. The surrogate model and the optimal solution evolve until the desired objective is achieved. The approach is tested on both the simulations of dielectric and viscoelastic properties in nanocomposites. Our work provides insight into identifying the interphase properties for polymer nanocomposites using adaptive optimization and demonstrates the potential of data-driven approach for achieving a deeper understanding of the interphase properties and its origins.