NRT-HDR: Data-Driven Discovery and Engineering from Atoms to Processes (3DEAP)

Scientists and engineers play an important role in addressing critical challenges faced by society in energy security, environmental sustainability, and human health. This National Science Foundation Research Traineeship (NRT) award to the University of Minnesota will address these grand challenges by training graduate students to work across common disciplinary boundaries through the integration of traditional domain science with data-driven methods.

The project anticipates training 150 M.S. and Ph.D. students including 25 funded trainees across disciplines including chemical engineering, biological engineering, chemistry, and materials science. Trainees will leverage their knowledge in these core science and engineering disciplines and build upon this foundation with tailored education in scientific computing, machine learning, systems-level analysis, and personal and professional development.

Upon completion of this program, students will be uniquely prepared to solve complex, interdisciplinary problems that leverage their expertise in traditional and data-driven science and engineering.

Data science is beginning to shape the design of materials, chemicals, and pharmaceuticals, but the heterogeneous nature and scarcity of data relevant to these disciplines presents a major challenge. The cost of data acquisition necessitates the integration of computational research to predict outcomes and inform experimental design. Research in this NRT will combine atomistic simulations, machine learning, and experimental methods to build models that integrate multiple data sources and scales.

A defining feature of the proposed research will be the incorporation of systems engineering across these modalities to address process-level considerations related to the design of emerging chemical, material, and biological platforms. Research will address three core themes including the development of foundational tools for multiscale modeling and integrated materials and process engineering, the discovery and design of materials and processes for sustainable energy conversion and storage, and the development and optimization of new vehicles for drug delivery.

Beyond research, this NRT will enhance educational infrastructure through a convergent graduate curriculum that provides students from diverse backgrounds with core skills in scientific computing and an integrated education on the fundamentals of data science and its application to problems in science and engineering. Close collaboration with industrial partners will provide trainees with unique professional development opportunities and inform the research questions and educational content addressed in this program.

The overarching goal of this project is to establish a foundation for training scientists and engineers as disciplinary experts who can work seamlessly with digital technology to address grand societal challenges.

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.