Project Grant 2602117
Award Date 8/1/26
Completion Date 7/31/29
Dollars Obligated $578K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
Tucson, AZ 85721, USA
Similar Awards
- 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 awarded the University of California, San Diego $519,998 on October 1, 2025, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop a data-driven framework for designing multi-element-doped alkali-ion conductors optimized for ion and electron conduction. The research integrates artificial intelligence, machine learning, high-throughput computational modeling, autonomous synthesis, and in-situ characterization to screen thousands of dopant...
- The National Science Foundation awarded a $1,039,367 Project Grant to the University of Virginia under the Mathematical and Physical Sciences program (CFDA 47.049) for work running from March 15, 2022 to February 28, 2026. The award will support the development of an artificial intelligence-driven framework for the design and discovery of complex materials, with a focus on energetic materials of strategic importance to the Department of Defense and Department of Energy. Key activities include...
- The National Science Foundation Division of Materials Research awarded Florida State University three hundred thousand dollars on August 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to conduct collaborative research on ion transport in amorphous superionic conductors through integrated lattice dynamics, machine learning, and atomistic simulations. The research establishes scientific principles for designing amorphous, glassy ceramic solid electrolytes for...
- This $249,860 Project Grant from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049) supports research at the University of Houston to develop machine learning models to predict the mechanical properties of solid-state electrolyte materials for lithium-metal batteries. The goal is to identify electrolyte compositions that can suppress the growth of lithium dendrites, which can lead to battery failure. The research embraces the principles of...
- The National Science Foundation Division of Materials Research awarded $560,967 to the University of North Carolina at Charlotte on August 1, 2026, to develop physics-informed artificial intelligence generative models for crystal structure prediction and materials discovery. The project will design AI models that incorporate fundamental physics laws, local chemical bonding patterns, and crystallographic symmetries to predict how atoms pack into solid materials. The resulting framework will...
- This Project Grant award from the National Science Foundation (NSF) Division of Materials Research under the Mathematical and Physical Sciences program (CFDA 47.049) provides $298,474 to the University of California, Santa Barbara (UCSB) to develop a software infrastructure for multi-scale simulations of materials. The key deliverables include: (1) Enumeration tools to generate a database of crystallographic and non-crystallographic models for training machine-learned interatomic potentials...
- 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 Maryland Baltimore County $380,996 on August 15, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to conduct collaborative research on stress-sensitive mechano-electrochemical instabilities of lithium metal anodes. The project combines operando mechanical measurements and analytical modeling to establish fundamental design principles for mitigating morphological instabilities in...
- The National Science Foundation Division of Materials Research awarded Boise State University $100,000 on October 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to investigate electrochemically driven phase transformations in nanostructured metal oxides for advanced battery materials. The research will establish a scientific framework for understanding how electrochemical reactions can create metastable crystalline materials with enhanced electrochemical properties...