Project Grant 2522541
- This $1,060,000 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program aims to revolutionize the discovery of new solid-state materials for advanced energy storage, neuromorphic computing, and smart sensor applications. The project will leverage advanced artificial intelligence (AI), machine learning (ML), and automated synthesis tools to develop a transformative approach for designing solid-state ion conductors using multi-element...
- This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program provides $400,000 in funding to the University of North Carolina at Charlotte (UNC Charlotte) from October 1, 2025 to September 30, 2029. The project aims to revolutionize the discovery of new solid-state materials that can precisely control the mobility of ions and electrons, an essential step toward building the next generation of energy storage...
- This $620,000 Project Grant awarded by the National Science Foundation's (CFDA 47.049) Mathematical and Physical Sciences program supports research by Professors Francesco Paesani of the University of California, San Diego and Mircea Dincă of Princeton University. The project aims to investigate the molecular mechanisms governing ion transport in confined environments, such as metal-organic frameworks (MOFs), and use these insights to design improved quasi-solid-state electrolytes for energy...
- 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 $300,776 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports collaborative research to develop an autonomous experimental framework for materials discovery. The key objectives are to create machine learning agents that can coordinate multiple experimental tools, such as scanning probe microscopes, structural probes, and synthesis platforms, to accelerate the discovery and optimization of novel ferroelectric and...
- This $250,000 Project Grant award from the National Science Foundation's (NSF) Division of Materials Research (CFDA 47.049 - Mathematical and Physical Sciences) supports research to design and discover new "heteroanionic" materials containing multiple types of negatively charged anion atoms. The goal is to understand how the atomic-scale structure of these complex materials influences their electronic, magnetic, and optical properties, enabling the development of advanced materials for...
- 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...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $330,000.00 in funding to the University of Maryland, College Park to develop an autonomous experimental framework for materials discovery. The key products and services to be delivered under this award include strategies for building smart experimental systems that allow different scientific instruments - such as microscopes, structural...
- This $920,000 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative research to accelerate the discovery of lead-free perovskite nanomaterials for advanced electronics and quantum technologies. The project aims to establish networked "self-driving laboratories" that integrate automated flow chemistry, nanomaterials synthesis, and machine learning to rapidly explore and optimize novel semiconductor...
- This $499,995 Project Grant awarded by the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to Arizona State University's Division (doing business as Orspa) is funding a collaborative research project to accelerate the discovery of new superconducting materials. The research team, which includes experts in materials synthesis, local probes, and computation, aims to exploit the characteristics of known copper- and iron-based superconductors to design and...
This $519,998 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program aims to revolutionize the discovery of new solid-state materials that can precisely control the mobility of ions and electrons. The University of California, San Diego (UCSD) will leverage advanced artificial intelligence (AI), machine learning (ML), and automated synthesis tools to develop a transformative approach for designing solid-state ion conductors using multi-element doping. This data-driven framework will systematically investigate how co-doping influences ionic transport, electronic structure, and lattice stability across bulk phases, grain boundaries, and interfaces. The project will establish a new approach to achieve an optimal balance of ion and electron conductivities for targeted applications in energy storage systems, neuromorphic computers, and smart sensors, while ensuring material stability. The research will provide hands-on training opportunities in AI-driven materials discovery, fostering collaboration among U.S. and Canadian universities, national laboratories, and industry partners.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $520.0k | 9/10/25 |