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

About This Grant

The Center for Materials Data Science for Reliability and Degradation (MDS-Rely) is adding a new site at Carnegie Mellon University (CMU). The center is dedicated to enhancing the reliability and longevity of essential materials through data science. By leveraging advancements in computing and data analysis, MDS-Rely addresses critical issues in materials degradation, which is vital for a broad spectrum of industrial sectors. MDS-Rely could transform electronics, energy, aerospace, and manufacturing by enabling better materials selection, failure prediction, and performance optimization. It will also contribute to next-generation electronics, printed sensors, and coating technologies. The center's research aims to develop innovative solutions to improve material performance and reliability, thereby supporting the U.S. economy and national interests. Some examples of these interests include more reliable processes for additive manufacturing of parts, developing formulations for consumer products that reduce degradation, and better methods for quantifying degradation in products and processes. MDS-Rely's interdisciplinary approach brings together industry leaders, government labs, and academic experts to tackle grand challenges in materials science. The center's work will advance scientific knowledge and foster workforce development by training students and professionals in cutting-edge data science techniques. MDS-Rely focuses on advancing materials reliability through data science and machine learning. Machine learning enables researchers to build predictive models for how material properties depend on processing or how they degrade during usage from data. These models can then be used to predict material behavior, or to optimize material properties to mitigate degradation and to increase their reliability. The center's research thrusts include developing solutions for materials degradation, creating robust study protocols, and applying machine learning to predict material performance. CMU's unique contributions include expertise in machine learning, and in developing sustainable materials. CMU brings advanced manufacturing and characterization facilities to support interdisciplinary research between machine learning practitioners, materials researchers and industry participants. By fostering collaboration between academia, industry, and government, MDS-Rely aims to enhance material reliability, support workforce development, and drive technological advancements. The center's strategic goals have evolved to include a stronger focus on sustainability and the application of machine learning to new materials challenges. This approach ensures that MDS-Rely remains at the forefront of materials research, and furthers NSF’s mission by contributing to better engineered products and technology through improving the reliability and performance of critical materials. 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.