DMREF: Collaborative Research: A Data-Centric Approach for Accelerating the Design of Future Nanostructured Polymers and Composites Systems

The overall objective of this research is to employ a data-driven approach, grounded in physics, to integrate models that bridge length scales from angstroms to millimeters to predict dielectric and mechanical properties of polymer nanocomposites, and use a closed loop to optimize/design new materials. There are three major research topics of this project.

• Topic 1: Targeted Data Generation and Interphase Modeling: Development of models for the dielectric and mechanical properties of the interphase based on constituents, DFT, atomistic, and QSPR approaches and curated and measured data from AFM/EFM property maps.
• Topic 2: Multiscale Modeling and Data Analytics: New methods combining data analytics and machine learning tools with physics based models, to bridge length scales, predict surface energies, interphase properties, and continuum level properties, including non-spheroidal fillers.
• Topic 3: Materials Design Methods and Tools: Development of new microstructure characterization and reconstruction methods pertinent to irregular geometries such as non-spherical fillers, new microstructure representation and machine learning approaches appropriate for managing the high dimensionality in processing-structure-property mappings, as well as a Bayesian optimization approach to guide “on-demand’ computer simulations and physical experiments in searching for optimal material design.