This $310,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of an innovative training program called CITEAM. The project aims to address gaps in computing and data management skills among the materials science research community that relies on advanced electron microscopy. CITEAM will provide training in areas such as microscope data processing, artificial intelligence...
This federal Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program, supports the development of an innovative training program targeting the materials science research community that relies on advanced microscopes. The project, titled "CITEAM," aims to address the gap in researchers' knowledge of data management, scientific programming, and utilizing computing resources to process and...
This National Science Foundation (NSF) Project Grant award for $333,028 under the NSF Computer and Information Science and Engineering (CISE) program (CFDA 47.070) funds a collaborative research initiative led by Rutgers, The State University of New Jersey. The project aims to address the challenge of training students, researchers, and instructors in computational tools and techniques at the chemistry, physics, and materials science interface. The four-year project will establish a robust...
This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) provides $491,583 to the Texas A&M Engineering Experiment Station (Tees) to develop a comprehensive education and hands-on training program that integrates computational materials science (CMS), machine learning/artificial intelligence techniques, and accelerated high-performance computing. The program aims to equip the future workforce with cutting-edge skills to drive innovation in...
This $199,040 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop new classes of computational algorithms that combine the benefits of direct computer simulations and the speed of machine learning predictions. The project, titled "XTRIPODS: HYBRID SCIENCE-MACHINE LEARNING SOLVERS FOR NANOPHOTONICS AND METAMATERIALS," will embed scientific knowledge into the machine learning...
The National Science Foundation Office of Advanced Cyberinfrastructure awarded a $333,333.00 Project Grant to the University of California, San Diego (UCSD) under the Computer and Information Science and Engineering program (CFDA 47.070) for the project "COLLABORATIVE RESEARCH: CYBERTRAINING: IMPLEMENTATION: MEDIUM: TRAINING USERS, DEVELOPERS, AND INSTRUCTORS AT THE CHEMISTRY/PHYSICS/MATERIALS SCIENCE INTERFACE". The goal of this 4-year project is to establish a robust U.S.-based...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CFDA 47.070) Project Grant, awarded to the University of Southern California (USC), provides $318,000 over 3 years to develop an innovative cyberinfrastructure training program for the synchrotron x-ray science research community. The project, titled "Collaborative Research: CyberTraining: Implementation: Medium: CyberInfrastructure Training and Education for Synchrotron X-Ray Science (X-CITE),"...
This $380,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports the Summer School for Integrated Computational Materials Education held at the University of Michigan from September 1, 2022 to August 31, 2025. The Summer School trains graduate students, postdoctoral fellows, and professors in computational and data science approaches for materials research and education to expand educational opportunities in computational...
This $499,999 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to develop a computational framework that accelerates microstructure prediction in additive manufacturing (AM) through physics-informed machine learning techniques. The project, awarded to Arizona State University (ASU), will involve designing novel graph neural networks to model the evolution of microstructures under...
This $798,445 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will support the acquisition of a field emission scanning transmission electron microscope (FE-STEM) at the South Dakota School of Mines and Technology (SDSM&T). The instrument will enable advanced materials research and characterization across multiple engineering and science fields, including energy conversion and storage, biomaterials, 2D materials, and materials for harsh...