The objective of this REU Site is to train a diverse group of early undergraduate students in the interdisciplinary skills necessary to employ a data-driven approach to materials design. REU students are learning to use advanced data analytics, such as machine learning and artificial intelligence, to efficiently identify trends in materials properties that can be used to predict the characteristics of undiscovered materials. This relatively new materials informatics approach promises to enhance efficiency and productivity, but also requires specialized training. This Site provides a comprehensive summer training experience that includes: introductory cross-training in data and materials science fundamentals, hands-on research training, formal educational seminars, and workshops. The Site focuses on providing these opportunities to students that typically do not have exposure to extensive scientific resources. All participants are prepared for careers in materials science in industry, academia, and national laboratories through customized professional development and networking opportunities in addition to extensive support and guidance.
increasing reliance on big data to guide innovation necessitates a change in the way that materials science students are trained. It has become essential for students to have experience in data-driven approaches and familiarity with the techniques that are now common to the field, yet these opportunities are very limited for undergraduates. This Site represents an entry point into this new approach to training, and prepares students for the emerging field of materials informatics. Training will include an introduction to essential topics such as material science applications, an introduction to data driven approaches, an introduction to machine learning with Python, and laboratory skill and safety training. All research topics are inherently interdisciplinary since the students are paired with primary and secondary mentors that specialize in different approaches to materials science. Scientific topics explored by the students can be divided into four categories, each with an inherent materials informatics component: (1) materials discovery and design; (2) high-throughput exploration; (3) quantitative structure–property relations; and (4) exploring and linking length scales. Taken together, these topics address a societal need for significant acceleration of new materials design and discovery, emergent properties and processing strategies. Reducing the cost and time it takes to discover and develop new materials and technologies can positively impact our economy, our environment, and our future.
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.
