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A team of experts from five universities (Duke, RPI, Caltech, Northwestern and Univ of Vermont) develops an open-source materials resource, NanoMine, to enable discovery of fundamental processing-structure-property (p-s-p) relationships for polymer nanocomposites and demonstrates extensibility through the creation of a sister resource for metamaterials, MetaMine. The framework enables annotation, organization and storage of composition, processing, microstructure and property data, along with an array of analysis tools and advanced learning algorithms that facilitate discovery of quantitative p-s-p relationships. A broad spectrum of users can query the system, identify materials that may have certain characteristics, and automatically produce information about these materials. The effort demonstrates the capability of the designed data framework through two domain case studies: discovery of factors controlling dissipation in nanocomposites, and tailored mechanical response in metamaterials motivated by an application to personalize running shoes. The project will significantly improve the representation of data and the robustness with which expanding user communities can identify promising materials applications, enabling new collaborations in materials discovery and design. Strong connections with the National Institute of Standards and Technology (NIST), the Air Force Research Laboratory (AFRL), and Lockheed Martin facilitate industry and government use of the developing knowledge graph.

A team of experts from five universities (Duke, RPI, Caltech, Northwestern and Univ of Vermont) develops an open-source materials resource, NanoMine, to enable discovery of fundamental processing-structure-property (p-s-p) relationships for polymer nanocomposites and demonstrates extensibility through the creation of a sister resource for metamaterials, MetaMine. The framework enables annotation, organization and storage of composition, processing, microstructure and property data, along with an array of analysis tools and advanced learning algorithms that facilitate discovery of quantitative p-s-p relationships. A broad spectrum of users can query the system, identify materials that may have certain characteristics, and automatically produce information about these materials. The effort demonstrates the capability of the designed data framework through two domain case studies: discovery of factors controlling dissipation in nanocomposites, and tailored mechanical response in metamaterials motivated by an application to personalize running shoes. The project will significantly improve the representation of data and the robustness with which expanding user communities can identify promising materials applications, enabling new collaborations in materials discovery and design. Strong connections with the National Institute of Standards and Technology (NIST), the Air Force Research Laboratory (AFRL), and Lockheed Martin facilitate industry and government use of the developing knowledge graph.