Project Grant 2311632
Award Date
10/1/23
Completion Date
9/30/28
Dollars Obligated
$2.3M
Federal Agency
Office of Advanced Cyberinfrastructure
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
New York, NY 10012, USA
Similar Awards
This $250,000 National Science Foundation project grant will support research at the University of Massachusetts Boston and the University of California-Santa Barbara to advance machine learning techniques for predicting the behavior of dynamic materials. Jason Green of UMass Boston and Igor Mezic of UC Santa Barbara will combine machine learning and physical theory methods to create new approaches for designing functional materials with tailored optical, mechanical, or photonic properties on...
This $569,490 federal Project Grant award was provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) to Michigan Technological University. The funding will support the development of a Bayesian symmetry-respecting machine learning framework for predicting electronic structures in materials design. The goal is to create a machine learning model that can accurately and efficiently predict electron density for a wide range...
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 National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) provides $310,000.00 to Arizona State University to investigate explainable machine learning models for predicting material properties from microstructural features. The 3-year project from May 1, 2025 to April 30, 2028 aims to develop new computational algorithms that can efficiently identify the critical n-point correlation functions needed to accurately model...
This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $300,000 in funding to the University of Maryland, College Park to develop a general-purpose foundation model for predicting rare events in atomistic simulations. The project aims to leverage advanced AI techniques like equivariant transformers, generative models, and multimodal learning to enhance prediction accuracy and...
This $299,918 Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure, under the Computer and Information Science and Engineering program (CFDA 47.070), will fund the development of portable machine learning models to support nuclear physics experiments at the Facility for Rare Isotope Beams (FRIB) in Michigan. The project will create pre-trained machine learning models using self-supervised techniques that can be quickly adapted by FRIB users for...
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's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $133,476 to Stanford University to develop machine learning models that can efficiently predict and analyze the fluctuations of biomolecules, such as proteins. The project aims to create scalable and transferable generative models that can construct accurate configurational ensembles of diverse molecular systems at a lower computational cost than traditional...
This National Science Foundation (NSF) Project Grant award under the Mathematical and Physical Sciences program (CFDA 47.049) is for $150,843 to develop an experimental framework for incorporating multimodal materials science data into large language models (LLMs) to enhance their ability to generate novel hypotheses for materials design. The 2-year project aims to expand LLM capabilities beyond text-based information by ingesting and learning from diverse experimental data formats like...
This four-year project grant from the National Science Foundation Division of Materials Research, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $1,414,494 to the University of Illinois to conduct collaborative research in machine learning algorithms for the prediction and synthesis of next-generation superhard functional materials. The research aims to advance scientific understanding and develop new materials through the use of machine learning to model material...