NRT: Data science-driven sustainability-centered advanced materials processing

About This Grant

Training methods in manufacturing have not kept pace with advances in how manufacturing processes are designed. Instead of teaching from a static handbook of unchanging material properties, novice scientists and engineers can now use AI-infused data science to understand and predict the complex range of properties and performance of materials as they are processed. In addition, traditional training does not include environmental, economic, and social sustainability considerations – from sourcing through processing to recycling, reuse, or disposal as part of materials selection or design although these aspects are increasingly demanded by end users. This skills gap necessitates a new educational paradigm in which data science is natively integrated into sustainable materials and process design, enabling consideration of the full life cycle of materials while accelerating their conceptualization and discovery. This National Science Foundation Research Traineeship (NRT) award to the Pennsylvania State University will equip the next generation of engineers, physical scientists, and social scientists with the tools required to effect transformative change in sustainable materials processing. The project (Sus-Mat for short) anticipates training 50 Ph.D. students, including 23 funded trainees, from Materials Science and Engineering, Chemical Engineering, Civil and Environmental Engineering, Computer Science and Engineering, Architecture, and Public Policy. This NRT will merge essential but commonly siloed fields of sustainability, data science, advanced materials processing, and public policy to create a holistic, data-driven materials and process design ecosystem. Trainees will learn to harness flexible data science tools including artificial intelligence (AI) integration, enabling them to understand how emergent processing approaches impact material properties and sustainability metrics and then employ those relationships to design sustainable materials and processes. The project will integrate the Sus-Mat themes of sustainability, data science, advanced materials processing, and public policy in pursuit of three core interdisciplinary research themes: (1) active learning for advanced materials processing optimization; (2) generative AI-based models for materials design; and (3) materials sustainability assessment framework. The interdisciplinary research will be enabled by the traineeship ecosystem consisting of: new core courses for foundational training, micro-credentials bolstered by experiential training, internships to facilitate knowledge translation, cohort-building activities to aid retention and community building, convergent research facilitated by co-advising, capstone experiences for broader outreach, and professional development that trains policy-savvy leaders in sustainable materials and process design. Sus-Mat’s combination of research projects using a range of materials, data availability, and processing technologies and skills training informed by public policy will modernize STEM workforce training in emergent materials processing technologies and accelerate the adoption of innovative, sustainable methodologies in high-tech domestic manufacturing with locally sourced materials. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, and 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.