Identifying interphase properties in polymer nanocomposites using adaptive 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.

Source: Composites Science and Technology - May 2, 2018 Category: Science Source Type: research