Nanostructured polymers are highly tailorable materials and potential game changers in a wide range of applications. Current nanocomposites are developed using an Edisonian process, severely limiting the capacity to optimize towards the required property space, and increasing time to implementation. To tackle this challenge in nanocomposite design we propose a new paradigm based on our main research hypothesis: by using a data-driven approach, grounded in physics, we can integrate models that bridge length scales from angstroms to millimeters to predict dielectric and mechanical properties and use a closed loop to optimize/design new materials . Central to this hypothesis are three sub-hypotheses: 1) Interphase Properties: Integrating a broad set of literature data and targeted experiments with multiscale methods, we can develop interphase models that predict local polymer properties near interfaces critical for bulk polymeric composites modeling, 2) Multiscale Modeling and Data Analytics: A hybrid approach utilizing machine-learning to bridge length scales between physics-based modeling domains can be used to create meaningful multiscale structure-property relationship work flows, and 3) Materials Design Methods: A Bayesian inference approach can utilize the knowledge contained in a dataset as a prior and guide “on-demand’ computer simulations and physical experiments to accelerate the search of optimal material designs. We will test these hypotheses through specific case studies focused on applications that require optimization of dielectric and mechanical properties.
|Effective start/end date||9/1/17 → 9/30/17|
- National Science Foundation (CMMI-1729743)
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