Advanced materials are important for many applications with societal benefits, including flexible electronics, solar panels, recyclable plastics, antibacterial coatings, water purification systems, and next-generation batteries. Properties of these materials depend heavily upon their structure or how various atoms and larger units are arranged. New instruments and mathematical models of materials allow scientists to measure and predict some of these properties. However, these techniques yield large quantities of data requiring complex analyses. This National Science Foundation Research Traineeship (NRT) award to Stony Brook University will train a diverse population of researchers with the skills to analyze the large data sets associated with complex material structures and properties. These students will participate in scientific communication, workplace diversity, career paths, and team-building workshops to meet the growing needs for a workforce trained in data science. Additionally, Stony Brook faculty will work together with the American Chemical Society Bridge Program and institutions that serve minority and disabled students to promote success of underrepresented students in science and engineering PhD programs. The project anticipates training seventy-five (75) PhD students, including thirty-eight (38) funded trainees, from the fields of chemistry, materials science, chemical engineering, mechanical engineering, and geosciences.
NRT trainees will develop new software tools and experimental methods for the materials community, contributing to improved design of advanced materials. Advances in instrumentation have enabled an unprecedented ability to probe structural dynamics that span many time and length scales, with incredibly high rates of data acquisition. Transformative aspects of the research include the development of new algorithms for rapid in situ X-ray spectroscopy of reactions; automated screening of parameter space of composites to advance the pace of materials discovery; and the first time-resolved measurements of nanoparticle motion during evaporative assembly. The main training elements are (1) interdisciplinary research on development of software and/or algorithms for data analysis; (2) a summer school that brings cohorts of diverse trainees together; (3) new elective courses and shorter modules on emerging computational and experimental tools; (4) hybrid group meetings centered around a common theme; (5) scalable responsible conduct of research, professional development, and career paths modules; (6) science communication training provided by the Alan Alda Center for Communicating Science; and (7) a student-driven career ladder program and guided mentorship through Individual Development Plans (IDPs) and activities tailored to students' career goals. To extend benefits beyond trainees, the project will implement a new graduate certificate on Quantitative Analysis of Dynamic Structures, transition the summer school to a self-sustaining program open to students nationwide, and distribute curriculum materials as open-access video lectures.
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.
