IUCRC Planning Grant Carnegie Mellon University: Center for Materials Data Science for Reliability and Degradation (MDS-Rely)

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This project proposes to plan a new site in the Center on Data Science for Materials Reliability and Degradation (MDS-Rely) by garnering industry support and feedback on applying data science-informed research to understand reliability and lifetime of materials, and use this feedback to ensure the Center is responsive to industry needs. Reliability of materials, parts and products are essential for US infrastructure in energy, defense, and national health and welfare. MDS-Rely will recruit potential industry partners interested in conducting research with the Center to apply data science to materials development. The planning grant will then garner feedback from these industry partners on the following objectives of the Center: (1) Enhance reliability testing, (2) Develop degradation models, (3) Apply degradation models, and (4) Develop educational programs. Together these efforts will enhance national interests. MDS-Rely will harness relationships between industry and academia to make headways in applying data science to applied materials development, design, and reliability. There will be three intellectual thrusts: (1) Soft and biomaterial formulation; (2) Natural language processing for data science of materials; and (3) Data-driven design of catalytic materials and processes. The soft and biomaterial formulation thrust will encompass machine-learning augmented design of experiments and automated science. The natural language processing thrust will focus on leveraging the existing literature more effectively in the area of materials reliability and degradation. Finally, the data-driven design of catalyst materials and processes will combine automated simulation with process systems engineering to discover materials and processes that are robust to degradation. The new site will leverage strengths at the intersections of computing, data science and machine learning with domain expertise in soft and biomaterials as well as catalysis at Carnegie Mellon University. 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.