IUCRC Phase I: University of Pittsburgh: Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

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The Center for Materials Data Science for Reliability and Degradation (MDS-Rely) is a joint industry/university cooperative research center (IUCRC) between Case Western Reserve University (CWRU) and University of Pittsburgh (Pitt). Data science-informed approaches can revolutionize the development, manufacturing, and lifecycle applications of materials, parts, and products for U.S. infrastructures in energy, defense, and transportation. MDS-Rely will improve the capabilities of students, employees, and the broader workforce by bringing together research and innovation in materials-based value chains to take on technological and societal challenges. The goals of MDS-Rely include developing solutions to materials reliability, degradation, and lifetime performance problems, based on robust open source codes and datasets, coupled with improved study protocols and standards that can be effectively implemented. The team also seeks to create a cross-cutting community to address the societal and materials challenges arising from degradation and failure of materials, components, and systems and to unite industry, national labs, and academic researchers to address these challenges by developing materials data science solutions. Finally, the team seeks to develop educational programs for a diverse STEM workforce prepared for dynamic careers in the materials and data sciences and to take on research problems with industrial and commercial impact. Through unique team research capabilities and infrastructure resources, MDS-Rely will develop reliability, performance, and degradation solutions, establish standards and reliability study protocols, and develop materials data science codes, packages and datasets to inform the materials value chains of polymers, elastomers, coatings, metals, alloys, semiconductors, and optoelectronics. This research will be achieved in three thrust areas: Weathering and Performance, Subtractive and Additive Manufacturing, and Components, Devices, and Systems. Projects include, but are not limited to: a) network models of mechanistic degradation pathways in materials and systems; b) machine learning on images to identify defects and the progress of degradation with exposure time; and c) identification of defects from tomographic images impacting the reliability of additively or traditionally manufactured parts. These new materials data science study protocols and analytics will enable MDS-Rely members to implement state of the art methods in their own research and manufacturing processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.