The National Science Foundation (NSF) awarded a $399,916 Project Grant under its Engineering program (CFDA 47.041) to the University of Nevada, Reno (UNR) to fund research on developing ultrastrong and ultraelastic metallic alloys using artificial intelligence (AI) enabled automated design. The goal of this 16-month project is to leverage AI, computational modeling, and experimental tools to rapidly design, synthesize, and test new metallic alloy compositions that can withstand extreme stress...
This $310,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Materials Research supports research at Arizona State University (ASU) to develop new computational algorithms for representing and modeling the microstructures of two-phase, disordered heterogeneous materials. The project aims to create an "explainable" approach that links material properties to microstructure morphology through physics-based governing equations. This will enable more...
This 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 the prediction of microstructures in additive manufacturing (AM) processes using physics-informed machine learning techniques. Specifically, the project will involve designing new graph neural network models to rapidly predict the evolution of material microstructures under...
This $1,481,906 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research to discover and develop new multi-principal-element hexagonal-close-packed structural alloys using advanced computational materials science and additive manufacturing methods. The University of California, Berkeley is the awardee, with the State of California Controllers Office serving as the parent organization. The project aims to rapidly...
This $335,707 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to investigate the dynamics of short-range order in multi-principal element alloys. The project will leverage artificial intelligence techniques to analyze atom probe tomography data and develop a mathematical model for the evolution of short-range ordering in these advanced...
This $500,001 Project Grant was awarded by the National Science Foundation (CFDA #47.070 - Computer and Information Science and Engineering) to the Texas A&M Engineering Experiment Station (Tees) to conduct research on advancing autonomous, materials-on-demand manufacturing capabilities through the convergence of manufacturing, AI, and materials science. The key objectives are to develop foundational principles for shape-constrained machine learning, effective handling of...
This $499,995 Project Grant awarded by the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to Arizona State University's Division (doing business as Orspa) is funding a collaborative research project to accelerate the discovery of new superconducting materials. The research team, which includes experts in materials synthesis, local probes, and computation, aims to exploit the characteristics of known copper- and iron-based superconductors to design and...
This NSF Mathematical and Physical Sciences (CFDA 47.049) project grant of $290,000 awarded to the New Mexico Institute of Mining and Technology (New Mexico Tech) will leverage atomistic simulations and machine learning to gain unprecedented insights into the complex multi-step crystallization processes that occur within metallic alloy melts. The research aims to develop a predictive modeling framework to understand the emergence of intricate transient liquid phase structures and their influence...
This $700,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support The Johns Hopkins University's research on amorphous metal additive manufacturing. The project aims to develop simulation-informed models and computational tools to enable design of additively manufactured amorphous metals with desired strength and toughness properties. This will involve using machine learning to quantify structural order parameters...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences (CFDA 47.049) program will provide $108,067 to the University of Arizona (doing business as the Arizona Board of Regents) from February 1, 2025 to January 31, 2030. The project aims to investigate how impurities and microstructure impact damage behavior in recycled aluminum alloys, with the goal of establishing compositional limits for high-performance applications. The research integrates...
This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) provides $425,943 to Arizona State University to develop an AI-enabled automated workflow for designing ultrastrong and ultraelastic metallic alloys. The research team aims to leverage artificial intelligence, computational modeling, and experimental tools to rapidly design, synthesize, and test these complex concentrated alloys. The innovative strategies developed through this 4-year project will have transformative impacts on metallic alloy design and manufacturing processes. Arizona State University will collaborate with partners like Stanford University and the University of Utah on the research activities. The project also provides broad education and outreach opportunities for students across materials science, computer science, and mechanical engineering disciplines, with a focus on promoting diversity, equity, and inclusion.