Project Grant 2602544
- 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...
- The National Science Foundation Division of Mathematical Sciences awarded Rensselaer Polytechnic Institute $269,117 on August 15, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop mathematical foundations and computational methods for simulating quantum materials. The award funds development of a rigorous and scalable framework for high-accuracy electronic-structure simulation of quantum materials using periodic coupled-cluster theory, addressing current...
- This $198,498 Project Grant awarded by the National Science Foundation (NSF) Division of Materials Research under the Mathematical and Physical Sciences program (CFDA 47.049) supports research at Drexel University aimed at developing a data-driven framework to predict synthesis pathways and optimal conditions for producing computationally-designed solid-state inorganic materials. The project will utilize deep learning, computational thermodynamic modeling, and validation experiments to...
- The National Science Foundation (NSF) awarded a $569,490 Project Grant under the Computer and Information Science and Engineering (CISE) program to Michigan Technological University on September 1, 2025. The goal of this 5-year project is to develop a Bayesian symmetry-respecting machine learning framework to accelerate the prediction of electronic structures in materials design. The research aims to address key challenges in current machine learning models, such as uncertainty quantification,...
- The National Science Foundation Division of Materials Research awarded the University of Arizona $578,383 on August 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop an artificial intelligence framework for generative modeling of atomic disorder in high-entropy oxide battery cathode materials. The project applies transformer-based machine learning, inspired by large language models, to learn and predict atomic arrangements in high-entropy oxides used in...
- The National Science Foundation Division of Materials Research awarded Massachusetts Institute of Technology $600,000 on July 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop data-driven materials-by-design strategies for organic mixed ionic-electronic conductive polymers and devices. The project integrates molecular design, advanced characterization, device engineering, and artificial intelligence across U.S. and Canadian research institutions to...
- The National Science Foundation (NSF) Division of Materials Research awarded a $262,500 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the Georgia Tech Research Corporation (Georgia Tech) for the "DMREF/Collaborative Research: Active Learning-based Material Discovery for 3D Printed Solids with Locally-Tunable Electrical and Mechanical Properties" project. This multi-disciplinary effort aims to establish an active learning approach to rapidly...
- This $563,899 Project Grant from the National Science Foundation's Division of Materials Research under the Mathematical and Physical Sciences program (CFDA 47.049) will fund research at the University of Virginia from September 1, 2022 to August 31, 2025. The research aims to advance fundamental understanding of the role and properties of crystal lattices in two-dimensional transition metal dichalcogenide materials across variable length and time scales. Specifically, the researchers will...
- 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...
- 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 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 determining when ordered or disordered structures form in materials critical to energy-efficient electronics and batteries. The work produces open-source software and datasets, develops educational resources at the intersection of materials science and machine learning, and trains students in computational materials design. The project is funded under the Mathematical and Physical Sciences program (CFDA 47.049).
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.9k | 8/3/26 |