Project Grant 2523285
Award Date 8/1/25
Completion Date 7/31/28
Dollars Obligated $330K
Federal Agency
Federal Grant Program
Assistance Type
Project Grant
Place of Performance
College Park, MD 20742, USA
Similar Awards
- 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 $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 federal Project Grant award, provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049), aims to accelerate the discovery of lead-free perovskite nanocrystals through the integration of high-throughput experimentation, artificial intelligence, and advanced data-sharing strategies across multiple institutions. The $600,000 award, with a performance period from October 1, 2025 to September 30, 2029, will fund the establishment of...
- 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'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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) provides $480,000 to establish a collaborative research effort focused on accelerating the discovery of lead-free perovskite nanomaterials. The project aims to integrate high-throughput experimentation, artificial intelligence, and distributed self-driving laboratories across multiple institutions to drastically shorten the timeline for discovering new semiconductor...
- This $200,000 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research effort led by professors at the University of Maryland and Iowa State University. The project aims to leverage artificial intelligence, robotics, and multi-scale materials chemistry simulations to accelerate the discovery of high-performance, biodegradable polymer nanocomposites with tunable properties. The integrated research...
- 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...
- This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) aims to revolutionize materials discovery by integrating physical principles into deep learning models. The $500,000 award, granted on June 15, 2025, with a completion date of November 30, 2026, will enable the Regents of the University of Minnesota to develop innovative machine learning techniques that can rapidly and...
- 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...