Over the last decade, there has been a shift in materials science research from slow individual experiments and computation to the beginnings of accelerated data-driven artificial intelligence (AI) approaches. Yet to achieve the promise of rapid discovery, design, and application of new materials, the development of a new generation workforce trained at the nexus of AI and materials is essential. This National Science Foundation Research Traineeship awarded to Duke University, AI for understanding and designing Materials (aiM), will provide integrated training for both materials and computer scientists, to advance the research and training frontiers of this new convergent field. Students will develop expertise in AI and materials science through a new curriculum bridging disciplines, linked with convergent research, professional skills, and external internships. This NRT will fill a critical gap in the advanced manufacturing workforce, facilitating future on-demand materials development for vital societal applications in flexible electronics, biomedical implants, infrastructure development, and many other areas. A total of 50 PhD students will be trained in the aiM program, 25 of whom will be NRT funded, from degree programs in computer science, data science, statistical science, and all materials disciplines including materials science, physics, chemistry, and all engineering fields with the goal of broadening participation of women and underrepresented minorities by recruiting a diverse group of undergraduates and promoting retention through culturally aligned mentoring and an inclusive climate.

The aiM program will deliver core elements designed to equip trainees with competitive 21st century professional and technical workplace skills. These core elements include: (1) newly developed transdisciplinary courses fusing data and materials science with problem- and project-based learning; (2) experiential learning through real-world application in internships with national lab or industry partners; and (3) professional development through boot camps, workshops, mentoring, outreach opportunities, and industry networking events. Students from both materials and computer-science domains will gain critical in-depth cross-training that integrates knowledge and methods across disciplines and enables development of new frameworks for discovery and innovation. New research frontiers will incorporate computational methods for different material classes, growing materials data warehouses for simulated and experimental data, and development and improvement of AI methods for scientific discovery. This NRT will impact students far beyond Duke through development of parallel open online course modules based on the fundamentals and applications of ?AI for materials? coursework and an annual aiM Challenge in which teams across the world can compete on a common materials data problem.

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.

National Science Foundation (NSF)
Division of Graduate Education (DGE)
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John Weishampel
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Duke University
United States
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