Project Grant 2544469
- This NSF Integrative Activities (CFDA 47.083) project grant, awarded to the University of Alabama at Birmingham (UAB) for $256,892.00, aims to develop a new machine learning-based computational method for simulating quantum effects in large-scale materials systems. The project, which runs from February 1, 2025 to January 31, 2027, will involve a collaboration with researchers at the University of Texas at Austin to apply techniques like density functional theory, Wannier functions, and deep...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, CFDA 47.049, supports theoretical and computational research and education to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for studying the electronic structure of materials. The $220,991 award aims to develop innovative machine learning-based approximations to the exact functional within density functional theory, which is critical for...
- This $700,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will fund the development of machine learning tools and quantum-classical hybrid algorithms to advance quantum materials research and optimize the use of noisy intermediate-scale quantum devices. Led by Cornell University with subawards to Princeton University, New York University, and Harvard University, the project will deliver new topological materials, a...
- Federal Project Grant Award Summary The National Science Foundation (NSF) Division of Mathematical Sciences awarded a CAREER grant totaling $296,354 to the University of California, Berkeley, effective July 1, 2026, through June 30, 2031, under the Mathematical and Physical Sciences program (CFDA 47.049). This project grant funds the development of quantum-inspired relaxation and embedding frameworks for scalable scientific computing in high-dimensional systems. The research addresses...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) will provide $277,686 to the Regents of the University of Minnesota to develop advanced computational modeling and machine learning workflows for exploring the mechanical and electronic properties of 2D quantum materials. The project aims to enable rapid, automated, high-fidelity simulations of these materials, which are critical for advancing emerging...
- Federal Grant Award Summary The National Science Foundation's Division of Information and Intelligent Systems awarded $600,000 through the Computer and Information Science and Engineering program (CFDA 47.070) to the University of California, Irvine for a three-year project (July 1, 2026–June 30, 2029) titled "Quantum Algorithms for Constructing Ground and Gibbs States." This project develops and analyzes quantum algorithms designed to prepare ground states and thermal (Gibbs) states...
- Dr. Henrik R. Larsson at the University of California, Merced received a $700,000 CAREER award from the National Science Foundation (NSF) Division of Chemistry under the Mathematical and Physical Sciences program (CFDA 47.049) to develop advanced computational methods for simulating quantum dynamics in complex molecules. The primary deliverables include tree tensor network state methods for high-dimensional molecular quantum dynamics and spectroscopy, new approaches for representing molecular...
- The National Science Foundation (NSF) awarded a $420,000 Project Grant through the Mathematical and Physical Sciences (CFDA 47.049) program to Northeastern University. The 3-year grant, beginning August 1, 2024, will fund research focused on developing novel machine learning-inspired computational methods and advancing the understanding of quantum many-body systems. Key efforts include: 1) applying neural network models based on "quantum attention" to study quantum many-body ground...
- The National Science Foundation (NSF) awarded a $569,490 Project Grant under the Computer and Information Science and Engineering (CISE) program to Michigan Technological University on September 1, 2025. The goal of this 5-year project is to develop a Bayesian symmetry-respecting machine learning framework to accelerate the prediction of electronic structures in materials design. The research aims to address key challenges in current machine learning models, such as uncertainty quantification,...
- Federal Grant Award Summary The National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) awarded $500,000 to The Research Foundation For The State University of New York on September 1, 2025, for a four-year implementation project (through August 31, 2029) titled "CyberTraining: Implementation: Small: Modeling Quantum Dynamics of Excited States in Materials in the Era of Machine Learning." The project addresses a critical national...
CAREER: Machine Learning Methods for Accelerating Large-Scale Quantum Mechanical Simulations The National Science Foundation (NSF) Division of Computing and Communication Foundations awarded $426,864 to the University of California, Berkeley under the Computer and Information Science and Engineering program (CFDA 47.070) on July 1, 2026, to develop machine learning frameworks that accelerate quantum mechanical simulations while maintaining physical accuracy. The research addresses the computational trade-off between quantum mechanical precision and simulation speed by creating machine learning tools guided by empirical scaling laws that integrate physical principles to predict atomic interactions across diverse environments. This capability will enable exploration of larger molecular systems and longer timescales than currently feasible, directly supporting applications in drug discovery and energy technologies. The project integrates technical research with workforce development objectives, establishing new interdisciplinary training programs and cross-disciplinary curricula to prepare researchers across multiple scientific fields in these advanced computational methods. The five-year award, extending through June 30, 2031, includes no planned sub-awards and reflects NSF's commitment to fundamental research that bridges theoretical models with large-scale practical simulations. The research directly supports the broader NSF portfolio emphasis on artificial intelligence and machine learning applications within computing and information science.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $426.9k | 7/1/26 |