This $148,554 Project Grant awarded by the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering program (CFDA 47.070) aims to develop an open-source, high-fidelity materials database for quantum materials. The key products and services to be delivered are: Implementation of high-throughput algorithms using modern quantum many-body methods, including a combination of Density Functional Theory (DFT) and Dynamical Mean...
This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) supports research and education to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for understanding the electronic structure of materials. The $220,991 award to the University of California, Irvine aims to develop innovative machine learning-based approximations to the exact functional used in density functional theory...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program, with Federal Grant Program ID 47.049 and CFDA Number 47.049, provides $232,250 in funding to The Research Foundation For The State University Of New York (Stony Brook University) from November 15, 2024 to October 31, 2027. The award supports theoretical and computational research and education to enhance the accuracy and efficiency of density functional theory (DFT) simulations, a...
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 National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) will support $314,460 in research aimed at advancing the understanding of quantum materials and their potential for enabling next-generation technologies. The 3-year project, starting on August 1, 2025, will focus on developing new models and methods for designing quantum materials by linking continuum mechanics with quantum field, electric field, and magnetic field interactions. Key objectives...
This $296,555 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support the development of reduced basis enhancements for neural networks and their application to quantum materials simulation. Specifically, the University of Massachusetts Dartmouth will combine traditional reduced basis methods with deep learning techniques to build an analysis-driven computational emulator for parameterized partial differential equations....
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...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support research to develop provably correct implementations of density functional theory (DFT) approximations. DFT is a widely used computational method in fields like solid state physics, chemistry, and materials science. The $625,840 award, effective June 15, 2025 through May 31, 2029, will fund the development of formal methods to...
The National Science Foundation (NSF) awarded Duke University a $200,000 project grant under the agency's Mathematical and Physical Sciences program (CFDA 47.049) to support the "Accelerated Design, Discovery, and Deployment of Electronic Phase Transitions (ADEPT)" research project. The goal of this 4-year effort is to develop integrated protocols that transform the standard sequential materials discovery process into a closed-loop, iterative approach. This will enable the...
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...