Collaborative Research: CyberTraining: Implementation: Medium: The Informatics Skunkworks Program for Undergraduate Research at the Interface of Data Science and Materials Science

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The project will develop a sustainable and scalable approach to train a workforce skilled in research and application of machine learning (ML) in materials informatics. ML in materials informatics is rapidly transforming materials science and engineering (MS&E) by an unprecedented ability to extend materials databases, improve materials simulation, mine texts, automate materials research and development, and accelerate materials design. As pointed out by multiple recent studies, including from the National Academies and the Minerals, Metals & Materials Society (TMS), it is essential to train a next generation workforce in ML for materials informatics to realize its enormous potential for improving the human condition through advanced materials. Unfortunately, ML in materials informatics is almost completely absent from today's materials curricula at the undergraduate level. However, the power of informatics tools combined with their rapid evolution and relative novelty in MS&E creates an opportunity for engaging undergraduates (UGs) with active learning through impactful research. With this motivation, the project will create new infrastructure and an ecosystem for the engagement and training of UGs across the U.S. in research using applied ML in materials informatics, called the Informatics Skunkworks. The Skunkworks consists of mentor/UG teams performing research in materials informatics. The project provides the teams with new resources, consisting of curricula, software, and research problems, and with a community of practice to support research and to work effectively and collaboratively. The low cost and accessibility of ML and materials informatics creates an opportunity for Skunkworks to engage mentors and students with limited research resources, particularly at institutions serving underrepresented groups. The Skunkworks is a sustainable and scalable approach that can fulfill this unmet need by training a diverse workforce skilled in research and application of ML for materials informatics. The project will provide freely available (a) curriculum to train UGs in relevant ML, materials informatics and research professional development, (b) software tools that augment existing ML packages to be UG accessible, and (c) authentic and appropriate-level research problems. The proposed work will also develop a community of practice to enable a network of productive mentor/UG research teams to effectively and collaboratively use the curricular, software and other resources developed by the project to support transforming the future workforce. The intellectual merit of the proposed work is to (a) develop scalable resources to increase UG experience and learning in research at the boundary of data science and materials science and engineering, (b) grow a community of mentors and UG researchers engaged in materials informatics research, and (c) increase the utilization of data science tools for solving critical problems in MS&E and related fields through workforce development and materials informatics training. The broader impact of the proposed work is to (a) freely disseminate enabling curricula and tools for materials informatics, (b) train staff, mentors and UGs in broadly applicable research and professional skills, and (c) develop a diverse community of practice for materials informatics researchers. The project will enable the development of a new workforce capable of advanced materials informatics, especially for underrepresented groups, by supporting primarily UG institutions and community colleges that often have limited research resources. This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering, with the Division of Materials Research in the Directorate for Mathematical and Physical Sciences also contributing funds.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.