NRT-AI: Harnessing AI for Inverse Design Training in Advanced and Sustainable Composites (IDeAS Composites)

Despite the vast design space of composites, there are significant gaps between the performance, economic, and environmental targets and current design and manufacturing approaches. Most egregious are the expensive, long development cycles and the sub-optimal design that waste resources and may adversely affect the environment and climate change. The fundamental cause of such gaps is the lack of detailed understanding of the influence of the material architecture, process methods, and parameters on material microstructure evolution and subsequently the end product’s physical, economic, and environmental performance.

This National Science Foundation Research Traineeship (NRT), harnessing artificial intelligence (AI) for Inverse Design Training in Advanced and Sustainable Composites (IDeAS), will train students through a physics-informed, AI-based modeling and design platform which will enable the discovery of new composites materials forms and relevant new manufacturing methodologies. This NRT award to Clemson University will catalyze a shift in the research and discovery pathway of the trainees via a transformative AI-age curriculum co-instructed by academic faculty and industry researchers.

The IDeAS Composites program will train a total of 50 students; of these, 25 will be NRT-funded IDeAS fellows and the remaining 25 would be identified as IDeAS scholars. The program will draw trainees from computer science, data science, statistical science, mechanical engineering, automotive engineering, and materials science and will empower trainees with an academia–industry co-trained skill set that will ensure their success in the AI age.

The research theme of this NRT program is focused on discovering and investigating the effectiveness of a physics-informed, machine-learning-based inverse design platform for developing new composite material architectures and manufacturing methodologies. The program will train a cohort of graduate students with deep, specialized expertise supported by broad, cross-skill knowledge, and equip them with a unique “DNA-shaped” skill set collaboratively facilitated by both academic and industry experts.

Specifically, the program will (1) catalyze interdisciplinary research at the intersection of AI and the inverse design of composites and manufacturing innovation via constructing a “digital life cycle” which is a suite of high-fidelity models for simulating a composite component’s life cycle, investigating the application of machine-learning methods for inverse composite material architecture and manufacturing process design, and developing an inverse design approach for integrated material and manufacturing design; (2) explore a combined graduate and undergraduate student training model comprising a composites inverse design capstone and a research design, development, and demonstration (RD&D) project centering on research outcomes applied to industry problems; (3) create a diverse, equitable, and inclusive environment fostering interdisciplinary collaboration in which trainees are prepared for careers requiring a unique “DNA-shaped” skill set; and (4) establish an interdisciplinary education program to (a) prepare next-generation composites engineering graduates (altogether 50 by year 5) who will have AI-enabled inverse design expertise and skills necessary to meet the unique challenges of the coming AI age and to thrive in the composites industry, and (b) train the current workforce to enhance their knowledge and foster dissemination of AI methods and principles in the composites engineering community.

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.