Project Grant 2347218

Award Date 9/1/24
Completion Date 8/31/27
Dollars Obligated $182K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Ames, IA 50011, USA
Similar Awards
This National Science Foundation (NSF) Project Grant award under the Engineering program (CFDA 47.041) supports the development of a multiscale simulation tool that uses machine learning to predict the deformation behavior of semiconductors under light exposure. The $316,455 award to North Carolina State University will establish an electronic-to-mesoscale modeling framework to advance the understanding of "photoplasticity" - the phenomenon where light can cause materials to harden...
This $150,000 project grant from the National Science Foundation's Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of GPU-accelerated first-principles simulation techniques to model exciton dynamics in complex materials systems over three years. Led by the University of Illinois at Urbana-Champaign, the collaborative research effort will implement novel methods for describing quantum-mechanical...
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 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 $288,229 federal Project Grant awarded by the National Science Foundation (NSF) Division of Materials Research supports theory, computation, and education to advance the engineering of nanoparticle-based materials. The project aims to develop a comprehensive computational framework to predict the structure and properties of functional nanomaterials, which will be made available to the broader research community. The work will be conducted at Iowa State University and involve collaborative...
This $589,486 National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) Project Grant award, effective September 1, 2023 through August 31, 2028, supports research by Iowa State University of Science and Technology to develop an integrated experimental-computational framework for understanding the fundamental mechanics of crumpled nanostructures and how surface adhesion can enhance their load-bearing capacity and defect tolerance. The research will...
This National Science Foundation (NSF) Engineering program (CFDA 47.041) project grant award of $179,460 supports fundamental research to develop a novel machine learning framework for predicting and preventing cracking in semiconductor materials, specifically at silicon carbide/aluminum nitride (SiC/AlN) interfaces during the cooling process. The research aims to use advanced machine learning and simulation techniques to identify the mechanisms of cracking and proactively prevent it, which...
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 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 from the National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation supports research to advance manufacturing processes for semiconductor electronic and quantum devices at the atomic scale. Funded for $292,392 over two years from January 1, 2023 through December 31, 2024, the award supports the University of Maryland, College Park under the NSF Engineering program (CFDA 47.041). The research aims to develop robust manufacturing techniques with...

This federal Project Grant award, funded by the National Science Foundation's Engineering program (CFDA 47.041), supports the development of a multiscale simulation tool that fuses quantum, atomistic, and mesoscale models through machine learning. The goal is to establish a simulation framework and database that can advance the understanding of how semiconductors deform under light exposure, a phenomenon known as photoplasticity.

The $182,000 award to Iowa State University of Science and Technology aims to create a large-scale dataset from density functional theory calculations, consolidate it into machine learning-based force fields, and incorporate those into nanoscale molecular dynamics and mesoscale coarse-grained models. This multiscale computational framework and knowledge database can be applied to study photo-, electro-, and chemo-plasticity in various materials and devices, where the interaction between electrons and defects is crucial but difficult to probe experimentally. The award period is from Sep 1, 2024 to Aug 31, 2027.

Generated 5/13/25, 3:41 AM