Project Grant 2317008
- This $344,743 project grant awarded by the National Science Foundation (CFDA 47.041 - Engineering) to Portland State University will develop advanced computational methods to simulate lattice vibrations and heat transfer in complex materials beyond the standard quasiparticle approximation. The research aims to provide more reliable predictions of thermal properties, especially in technologically important materials like thermoelectrics and thermal barrier coatings. The project outcomes will...
- The National Science Foundation (NSF) Division of Materials Research awarded a $100,000 Project Grant to Northwestern University to develop a comprehensive phonon database and related analysis, visualization, and prediction tools. The grant, under CFDA Program 47.049 Mathematical and Physical Sciences, aims to generate a database of 40,000 phonon dispersions and 15,000 lattice thermal conductivity properties, as well as phonon data for 300,000 predicted structures. The project will create a...
- 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) awarded a $201,741 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Portland State University to develop a predictive framework for harnessing order-disorder phenomena in mixed-chalcogen semiconductor materials. The 3-year project, running from October 2025 to September 2028, will combine computer modeling, machine learning, and laboratory experiments to better understand how the arrangement of atomic anions impacts the...
- This National Science Foundation project grant award of $325,441 will fund research into chemically tunable acoustic phonons in two-dimensional materials under the Mathematical and Physical Sciences program (CFDA 47.049) from September 1, 2022 to August 31, 2025. Professor Kristie Koski at the University of California, Davis will study acoustic phonons, or sound waves that control material properties, in new two-dimensional materials only a few atoms thick using precise laser spectroscopy...
- 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...
- 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...
- 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 Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to revolutionize materials discovery by integrating physical principles into deep learning models. The $500,000 award, granted on June 15, 2025, with a completion date of November 30, 2026, will enable the Regents of the University of Minnesota to develop innovative machine learning techniques that can rapidly and...
- This Project Grant award of $249,556 from the National Science Foundation (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports the development of a new computational method to accelerate quantum embedding simulations for strongly correlated materials. The project aims to apply machine learning techniques, specifically dimensionality reduction, to construct compact variational subspaces that approximate the low-energy manifold relevant to embedding calculations. The resulting...
This Project Grant award from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049) aims to develop a computational framework using advanced machine learning techniques to model atomic vibrations and their effects on material properties. The $249,726 award to Portland State University supports research to: 1) create universal machine learning force fields using graph neural networks for phonon modeling, 2) expand a phonon database through active learning, and 3) gain a data-driven understanding of atomic vibrations in thermodynamic stability and heat transfer. This multi-disciplinary effort integrates advanced simulations, high-throughput calculations, and machine learning to deepen the understanding of phonons and enable more accurate prediction of material behaviors, especially for applications like high-entropy alloys and thermal management. The project also supports the education and training of undergraduate and master's students in computational materials science.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $249.7k | 8/7/23 |