Project Grant 2602545
- The National Science Foundation Division of Materials Research awarded Rensselaer Polytechnic Institute $300,880 on August 15, 2026, to develop a motif-based deep learning framework for predicting and interpreting structural disorder in crystalline solids, with Zintl phases as the model system. The project runs through July 31, 2029, and is performed in Troy, New York. The research links quantum mechanical simulations, materials databases, and deep learning to uncover chemical principles...
- The National Science Foundation Division of Materials Research awarded the University of Utah $379,380 on March 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to establish molecular-level design rules for chirality transfer in conjugated polymer systems. The research will develop flexible, lightweight materials that can be electrically reprogrammed in real time to change how they interact with light and electronic signals. The work combines conducting plastics with...
- The National Science Foundation Directorate for Mathematical and Physical Sciences awarded the University of Utah $250,000 on September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop high-fidelity generative mean flow models that integrate artificial intelligence with computational mathematics for scientific machine learning applications. The research addresses computational accuracy and speed challenges in flow-based generative models by establishing a...
- The University of Utah received a two-year, $449,988 Project Grant award from the National Science Foundation Division of Materials Research under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The grant will support experimental characterization of microscopic properties of superconducting polyhydrides to develop a more realistic theoretical framework for warm superconductivity. Work will be performed in Salt Lake City, Utah from August 2021 through July 2023. The...
- The University of Utah received a $448,560 Project Grant award from the National Science Foundation Division of Materials Research on August 15, 2021 to fund research under the EAGER: SUPER project through July 31, 2023. The grant supports research seeking to discover high-temperature superconductivity in heterostructured two-dimensional organic materials. This work aligns with the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049), which aims to advance...
- This $106,769 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program will support the development of new methods to systematically explore and predict materials microstructures. The University of California, Davis will receive funding from September 1, 2022 through August 31, 2024 to adapt machine learning and data science techniques for materials science applications. Specifically, the university will integrate expert knowledge on physically meaningful...
- The National Science Foundation Division of Materials Research awarded Colorado State University $360,004 on July 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop machine learning protocols for designing protein-DNA co-crystal materials that function as three-dimensional scaffolds for capturing and determining the atomic-level structures of biological molecules via X-ray crystallography. The award supports research aimed at accelerating structure...
- 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...
- The National Science Foundation Division of Materials Research awarded Boise State University $344,276 on September 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop a deep learning framework that estimates material parameters in multi-principal element alloys directly from microstructure images. The award is a Project Grant with period of performance through August 31, 2029, and place of performance in Boise, Idaho. The research addresses a critical...
- The National Science Foundation awarded a $310,000 Project Grant to Arizona State University (ASU) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The grant, which runs from May 1, 2025 to April 30, 2028, supports research to develop explainable machine learning models for predicting material properties based on their microstructure. By systematically learning the key n-point correlation functions that drive material behavior, the project aims to provide both...
The National Science Foundation Division of Materials Research awarded the University of Utah $328,828 on August 15, 2026, for collaborative research developing a motif-based deep learning framework to predict and interpret structural disorder in crystalline solids. The award, supported under the Mathematical and Physical Sciences program (CFDA 47.049), funds research using Zintl phases as a model system to link quantum mechanical simulations, materials databases, and deep learning to uncover chemical principles governing ordered or disordered material structures. The project delivers open-source software and datasets, educational resources at the intersection of materials science and machine learning, and student training in computational materials design. Work is performed in Salt Lake City, Utah, with a period of performance through July 31, 2029. The research strengthens computational materials discovery capabilities for advanced energy and electronic technologies by enabling more accurate and interpretable prediction of complex materials that exhibit finite-temperature site disorder influencing stability and functional properties.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $328.8k | 8/3/26 |