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Title: Materials Informatics and Data System for Polymer Nanocomposites Analysis and Design
The application of Materials Informatics to polymer nanocomposites would result in faster development and commercial implementation of these promising materials, particularly in applications requiring a unique combination of properties. This chapter focuses on a new data resource for nanocomposites — NanoMine — and the tools, models, and algorithms that support data-driven materials design. The chapter begins with a brief introduction to NanoMine, including the data structure and tools available. Critical to the ability to design nanocomposites, however, is developing robust structure–property–processing (s–p–p) relationships. Central to this development is the choice of appropriate microstructure characterization and reconstruction (MCR) techniques that capture a complex morphology and ultimately build statistically equivalent reconstructed composites for accurate modeling of properties. A wide range of MCR techniques is reviewed followed by an introduction of feature selection and feature extraction techniques to identify the most significant microstructure features for dimension reduction. This is then followed by examples of using a descriptor-based representation to create processing–structure (p–s) and structure–property (s–p) relationships for use in design. To overcome the difficulty in modeling the interphase region surrounding nanofillers, an adaptive sampling approach is presented to inversely determine the inter-phase properties based on both FEM simulations and physical experiment data of bulk properties. Finally, a case study for nanodielectrics in a capacitor is introduced to demonstrate the integration of the p–s and s–p relationships to develop optimized materials for achieving multiple desired properties.  more » « less
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Handbook on Big Data and Machine Learning in the Physical Sciences
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National Science Foundation
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