NRT-QL: Interdisciplinary Graduate Program in Quantum Materials Science and Engineering

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Harnessing and engineering the useful properties of materials has been a recurrent theme in the history of human advancement from mastering metallurgy in the Bronze age to creation of semiconductor devices in today’s digital society. Quantum materials, which are materials where quantum mechanical effects are critical to their useful properties, have played an important part in this technological progress, but are poised to do so even more. For example, there is growing interest and excitement about quantum computers, as well as both government and industry funding of research and development in the quantum sciences. This National Science Foundation Research Traineeship (NRT) award to Yale University will create an interdisciplinary Ph.D. program on quantum materials and prepare trainees for impactful professional careers in the field of quantum materials science and engineering. Additionally, the program will strengthen and diversify the US scientific and technical workforce in the quantum sciences. This program will train 30 Ph.D. students, including 17 funded trainees, from the disciplines of Applied Physics, Chemistry, Computer Science, Mechanical Engineering and Materials Science, and Physics. The program will provide training through education, research, industry and national laboratory summer internships, and professional development. Trainees will become experts in creating, using, and understanding quantum materials, while gaining firsthand knowledge of developing skills in team science, science communication, outreach, teaching, and mentoring. The research plan of this NRT program focuses on three related themes: (a) understanding the growth processes of topological crystalline nanowires at the atomic scale, (b) creation and engineering of nanowire-based topological interconnects and their electron transport properties, and (c) topological superconducting nanowires for potential quantum bit storage. The interdisciplinary and collaborative research effort will bring together experimental efforts in materials growth, characterization via electron transport and surface microscopy, and computational work in electronic structure calculations, machine learning, and molecular dynamics simulations. For example, for theme (a), first-principles electronic structure calculations will provide structural and energetic data on likely materials configurations to train machine learning models to produce high quality classical potential energy functions. These can be used for large-scale and long-time molecular dynamics simulations of the material growth process; the key structural configurations found in the dynamics will then be fed back to the first principles calculations to recalibrate and refine the machine-learned potentials in a closed-loop manner. A key feature of the traineeship is a new flagship course, containing hands-on modules allowing trainees to analyze data from various NRT program-related research laboratories, that will introduce quantum materials science and engineering, the NRT trainers, and research projects to the trainees. 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.