Project Grant 2602213
- The National Science Foundation awarded a $1,039,367 Project Grant to the University of Virginia under the Mathematical and Physical Sciences program (CFDA 47.049) for work running from March 15, 2022 to February 28, 2026. The award will support the development of an artificial intelligence-driven framework for the design and discovery of complex materials, with a focus on energetic materials of strategic importance to the Department of Defense and Department of Energy. Key activities include...
- The National Science Foundation Division of Materials Research awarded the University of Utah $328,828 on August 15, 2026, for collaborative research developing a motif-based deep learning framework to predict and interpret structural disorder in crystalline solids. The award, supported under the Mathematical and Physical Sciences program (CFDA 47.049), funds research using Zintl phases as a model system to link quantum mechanical simulations, materials databases, and deep learning to uncover...
- ACED: Accelerating Materials Discovery by Learning with Physics-Informed Constraints The University of Minnesota received a $500,000 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems (Computer and Information Science and Engineering program, CFDA 47.070) to develop advanced machine and deep learning (MDL) models for accelerated materials discovery. The project, awarded June 15, 2025, with completion targeted for November 30, 2026, aims...
- The National Science Foundation (NSF) Division of Materials Research awarded a $346,461 project grant to North Carolina State University (NC State) under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports research and education aimed at advancing simulation and computational approaches for studying the atomic and electronic structures of materials. Key focus areas include: Expanding the ability of quantum Monte Carlo (QMC) many-body wave function methods to describe...
- The National Science Foundation Division of Materials Research awarded the University of Arizona $578,383 on August 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop an artificial intelligence framework for generative modeling of atomic disorder in high-entropy oxide battery cathode materials. The project applies transformer-based machine learning, inspired by large language models, to learn and predict atomic arrangements in high-entropy oxides used in...
- The National Science Foundation Division of Materials Research awarded Cornell University a $4 million cooperative agreement on October 1, 2025, under the Mathematical and Physical Sciences program (CFDA 47.049) to advance artificial intelligence-driven materials discovery through the AI Materials Institute (AI-MI). The award funds development of the AI Materials Science Ecosystem (AIMS-EC), an open, cloud-based platform coupling a science-ready large-language model with multimodal data...
- This Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research to develop a new class of ultralight, manufacturable materials with optimized mechanical and transport properties. The $362,000 award to the University of North Carolina at Chapel Hill aims to: Characterize the benefits of exploiting local uniformity and hyperuniformity in material design; Measure mechanical and transport properties to understand the...
- Federal Grant Award Summary The National Science Foundation (NSF), Division of Mathematical Sciences, awarded a $157,500 project grant to the University of North Carolina at Charlotte under the Mathematical and Physical Sciences program (CFDA 47.049) for a collaborative research initiative with the University of Warwick in the United Kingdom. The project, titled "A Novel Theory-Based Framework for Coarse-Graining and Simulating Stochastic Differential Equations for Crystalline...
- The National Science Foundation Division of Materials Research awarded Rensselaer Polytechnic Institute $300,880 on August 15, 2026, to develop a motif-based deep learning framework for predicting and interpreting structural disorder in crystalline solids, with Zintl phases as the model system. The project runs through July 31, 2029, and is performed in Troy, New York. The research links quantum mechanical simulations, materials databases, and deep learning to uncover chemical principles...
- This $198,498 Project Grant awarded by the National Science Foundation (NSF) Division of Materials Research under the Mathematical and Physical Sciences program (CFDA 47.049) supports research at Drexel University aimed at developing a data-driven framework to predict synthesis pathways and optimal conditions for producing computationally-designed solid-state inorganic materials. The project will utilize deep learning, computational thermodynamic modeling, and validation experiments to...
The National Science Foundation Division of Materials Research awarded $560,967 to the University of North Carolina at Charlotte on August 1, 2026, to develop physics-informed artificial intelligence generative models for crystal structure prediction and materials discovery. The project will design AI models that incorporate fundamental physics laws, local chemical bonding patterns, and crystallographic symmetries to predict how atoms pack into solid materials. The resulting framework will accelerate discovery, design, and screening of advanced functional materials, including complex porous solids and high-performance battery materials for next-generation energy storage and smart device applications. Work will extend through July 31, 2029, with performance based in Charlotte, North Carolina. The project is funded under the Mathematical and Physical Sciences program (CFDA 47.049). Beyond core technical innovation, the award supports a robust community and educational component. The newly developed AI tools and deep learning models will be released on open-source platforms with comprehensive documentation and free tutorials. The project will modernize graduate education through new interdisciplinary courses, hands-on learning modules, and a companion textbook. The team will also establish connections to industry through the North Carolina BATT CAVE research center and conduct interactive science workshops and laboratory tours for local K–12 students. An interdisciplinary team of materials experts and computer scientists will execute the work.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $561.0k | 7/31/26 |