NRT-HDR: Data and Informatics Graduate Intern-traineeship: Materials at the Atomic Scale (DIGI-MAT)

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

The application of engineering materials impacts every facet of society, through manufacturing, energy, transportation, medicine, and more. One the most important trends in materials science is the emergence of atomic scale characterization of materials. Characterizing and modeling materials at the atomic scale generates massive data sets. New advances in data science are now revolutionizing the way that materials data are captured, curated, managed, and manipulated. Industry and national labs are increasingly in need of a science and engineering workforce trained in both materials and data science. This National Science Foundation Research Traineeship (NRT) award to the University of Illinois will support the creation of a Data and Informatics Graduate Intern-Traineeship in Materials at the Atomic Scale (DIGI-MAT). The vision of DIGI-MAT is that materials problems will ultimately be data problems, and understanding of materials will be a challenge in capturing, curating, managing, and manipulating massive data streams. The program will combine cutting-edge research with a new curriculum, professional development opportunities, on-demand skills training, and, at the core of the program, research internships with external partners. This project anticipates training 72 PhD students, including 31 funded trainees, from degree programs in engineering, physics, statistics, and information science. The DIGI-MAT project balances research with coursework, skill-building, professional development and robust internships. Working across disciplinary boundaries, participants will tackle specific, high-impact research problems, including the following: (i) machine-learning to identify features in billion-atom microscope images; (ii) statistics and data-fusion methods for large materials data sets that vary over space and in time; (iii) new routes to automate the synthesis of nanomaterials using data; (iv) cyberinfrastructure for atomistic data; and (v) new highly accurate and efficient methods to computationally model materials. The program will integrate this research activity with new interdisciplinary graduate course offerings. Additional flexible training modules will be offered in data acquisition and management, materials data curation, uncertainty quantification, instrumentation for materials characterization, and application of machine learning to materials data. Trainees will be placed in semester-to-year-long research internships with partners in industry, national labs, academia, and abroad. Research internships will be closely aligned with trainees' graduate research, helping to prepare them for careers at the intersection of materials and data science. Finally, trainees will have the opportunity to develop additional career-aligned skillsets such as communication, leadership, and entrepreneurship through workshops and other venues (e.g. industry networking events). The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. 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.