Our objective is to formulate a novel, data-centric approach to accelerate the development of next-generation nanostructured polymers with unprecedented and predictable combinations of properties. To meet this objective, we are developing: a multiscale modeling strategy that bridges length scales by using machine learning approaches, physics based models, and a robust interphase model built using curated and custom generated data, as well as materials design methods and tools validated through test cases. Data and tools from the work are housed in an open data resource (NanoMine). Our approach advances materials design principles, demonstrated across a broad class of nano-reinforced polymeric composites.