Project Grant 2513830
- This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $137,978 to the University of Texas at Arlington (UTA) to develop a cloud-based, open-source multiscale modeling software called OpenMSG. The goal is to create an efficient prediction toolkit for analyzing the mechanical and multiphysics behaviors of highly heterogeneous materials and structures, such as advanced composites,...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports theoretical and computational research to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for understanding the electronic structure of materials. The $232,250 award to The Research Foundation for The State University of New York, operating as Stony Brook University, aims to develop innovative approximations to the exact...
- The Project Grant award of $625,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (MPS) program supports the development of computational simulation and machine learning tools for accelerating the discovery and design of functional organic materials that interact with light. The principal investigator, Daniel Tabor of Texas A&M University, is tasked with integrating machine learning and artificial intelligence methods to build new efficient searching...
- 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...
- The National Science Foundation (NSF), through its Mathematical and Physical Sciences (MPS) program (CFDA 47.049), awarded a $650,000 Project Grant to Yale University to develop new computational methods for simulating light-matter interactions in complex materials. Over a 5-year period from January 1, 2024 to December 31, 2028, the research team led by Dr. Tianyu Zhu will create a reliable and efficient toolbox for modeling spectroscopic properties of solid-state materials. This will involve...
- This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program totaling $449,995 supported research at the University of Texas at Austin from July 15, 2022 to June 30, 2025. The research aims to develop machine learning and parallel-in-time algorithms to efficiently simulate multiscale dynamical systems, reducing overall computation time for applications in physical science and engineering. Specifically, the researchers will construct effective solution...
- The National Science Foundation (NSF) awarded a $629,867 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to the University of Texas at Austin. The funding supports research by Dr. Doran Bennett to develop new computational methods for simulating excited-state dynamics and nonlinear spectroscopy of molecular materials at the mesoscale (10nm - 1μm). The project aims to establish how specific materials absorb and deploy energy from light, providing essential...
- This $252,532 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports the development of an open-source computational platform to accurately simulate the magnetic and electronic properties of strongly correlated materials (SCMs). The 3-year project (9/1/2025 - 8/31/2028) led by West Virginia University Research Corporation aims to incorporate dynamical fluctuation effects into first-principles methods to enable high-precision...
- This $2,000,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will accelerate the discovery of new superconducting materials through a transformative approach combining artificial intelligence (AI), quantum theory, and experimental synthesis. The research will integrate AI methods to predict superconductor properties and design novel, synthesizable materials with targeted superconducting and mechanical characteristics....
- This $399,998 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop innovative algorithms that integrate classical numerical schemes and deep learning techniques. The goal is to address complex scientific computing challenges, particularly in areas such as high-dimensional, fully nonlinear differential equations, long-time simulation of Hamiltonian systems, and boundary integral equations. The...
This $1,500,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports the development of MATCSSI 2.0, a cloud-integrated platform that streamlines complex many-body electronic structure calculations for materials modeling and design. The project aims to advance the predictive power and accessibility of many-body electronic structure methods beyond standard density functional theory. Key components include a cloud portal, interoperable software, a universal abstraction layer, intelligent user support, and end-to-end learning modules. This platform enables broader adoption of advanced computational materials science techniques by connecting to high-performance computing infrastructure. The University of Texas at Austin, a leading research university, is the primary awardee and will execute the project from October 2025 through September 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $1.5m | 8/4/25 |