ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Tackling society?s grand challenges will require a workforce that is prepared to work to integrate knowledge from multiple disciplines, to work as a team, and to leverage the intellectual contributions of individuals from diverse backgrounds. However, employers often find that graduate students entering the workforce lack interdisciplinary training and an appreciation for data science. These scenarios necessitate new designs of STEM graduate education programs that promote interdisciplinary team building, integration of data science in solution methodologies, and exposure of graduate students to multicultural work environments. This National Science Foundation Research Traineeship (NRT) award to The University of Akron (UA) will support the first such traineeship program in Northeast Ohio for masters and doctoral students working at the interface of materials science and machine learning that will lead to the discovery of new materials and major advances in materials research. The traineeship will serve 40 M.S. and Ph.D. students including 20 funded M.S. and Ph.D. students recruited from diverse backgrounds including underrepresented minority groups and enrolled in graduate programs in polymer science and polymer engineering, mechanical engineering, and computer science.

This NRT program seeks to identify and mitigate the knowledge gaps while providing data-driven answers for several cross-disciplinary research questions that are of interest to the broad materials research community. Examples include i) the tunability of a material modulus with temperature and the quality consistency of composite parts produced via additive manufacturing, ii) the dynamics of ions and mechanisms of ion transport in crystalline polymer domains as in batteries, iii) the molecular design of chemically recyclable polymers with tunable thermal and mechanical properties; iv) novel feature selection techniques in current machine learning tools to handle data from large complex polymeric systems; and v) efficient and systematic integration of large-scale heterogeneous data from multiple sources. The research will utilize a combination of experimental and numerical approaches, including molecular dynamics simulation, finite element analysis, polymer chemistry synthesis, additive manufacturing techniques and machine learning tools, to achieve the desired objectives. This project will create a scalable materials database that is integrable with existing polymer repositories and can be used to develop novel composite materials, molecular design rules for solid electrolytes with superior conductivity, and the design of new polymer materials that can be efficiently recycled. The trainees will develop strong interdisciplinary skills via courses and research work, workshops and seminars, laboratory rotations, internships at federal and industrial laboratories, and international exposure. They will receive a Data Science Engineering graduate certificate and are expected to become strong proponents of digitalization tools in materials research. The trainees will also participate in outreach and mentoring activities at high schools and two-year colleges to enhance diversity in STEM fields.

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.

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