Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN

Materials data management, analytics, and e-collaborations have been identified as three of the main technological gaps currently hindering the realization of the accelerated development and deployment of advanced materials targeted by the federal materials genome initiative. In this paper, we present our ongoing efforts aimed at addressing these critical gaps through the customized design and build of suitable data infrastructure elements. Specifically, our solutions include: (1) ELA—an experimental and laboratory automation software platform that systematically tracks interrelationships between the heterogeneous experimental datasets (i.e., provenance) acquired from diverse sample preparation and materials characterization equipment in a single consistent metadata database, (2) PyMKS—the first Python-based open-source materials data analytics framework that can be used to create high-fidelity, reduced-order (i.e., low computational cost), process–structure–property linkages for a broad range of material systems with a rich hierarchy of internal structures spanning multiple length scales, and (3) MATIN—a HUBzero-based software platform aimed at nucleating an emergent e-science community at the intersection of materials science, manufacturing, and computer science, and facilitating highly productive digital collaborations among geographically and organizationally distributed materials innovation stakeholders. This paper provides a timely report of lessons learned from these interrelated efforts.