Collaborative Research: CyberTraining: Implementation: Medium: Cyber Training on Materials Genome Innovation for Computational Software (CyberMAGICS)

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The computing landscape is evolving rapidly. Exascale computers can perform unprecedented mathematical operations per second, while quantum computers have surpassed the computing power of the fastest supercomputers. Concomitantly, artificial intelligence (AI) is transforming every aspect of science and engineering. To address these rapid changes and challenges, this project will train a new generation of materials cyberworkforce, who will solve challenging materials genome problems through innovative use of advanced cyberinfrastructure (CI) at the exa-quantum/AI nexus. Further, the project will foster the adoption of exa-quantum/AI nexus technologies by a broad research community and beyond through a unique dual-degree PhD/MS program, undergraduate research to close the research-education gap, and broadening participation of women and underrepresented groups.This project will develop training modules for a new generation quantum materials simulator named AIQ-XMaS (AI and quantum-computing enabled exascale materials simulator), which integrates exa-scalable quantum, reactive and neural-network molecular dynamics simulations with unique AI and quantum-computing capabilities to study a wide range of materials and devices of high societal impact such as optoelectronics and pandemic preparedness. CyberMAGICS (cyber training on materials genome innovation for computational software) portal will be developed as a single-entry access point to all training modules that include step-by-step instructions in Jupyter notebooks and associated tutorial slides/videos, while providing online cloud service for those who do not have access to computing platform. The modules will be incorporated into the open-source AIQ-XMaS software suite as tutorial examples, and they will be piloted in classroom and workshop settings to directly train 1,200 CI users at the University of Southern California (USC) and Howard University, with a strong focus on underrepresented groups. Broader reach and training will be accomplished through the portal and nanoHUB. Students trained in the dual-degree program will earn a PhD in materials science or physics; they will also earn either an MS in computer science specialized in high-performance computing and simulations, MS in quantum information science, or MS in materials engineering with machine learning. Undergraduate students will be mentored and trained by academic scholars in multidisciplinary fields as well as by scientists at national labs and industry. The project will further broaden participation through USC’s Women in Science and Engineering (WiSE) program and undergraduate research by underrepresented groups jointly supervised by USC and Howard faculty.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.