Project Grant 2523284
- 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 $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 $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 $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 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 Project Grant award, funded by the National Science Foundation's Engineering program (CFDA 47.041), aims to revolutionize chemical synthesis and composite material discoveries through an innovative physics-informed, machine learning-based adaptive design of experiments framework. The $578,793 award to Kansas State University, with a planned sub-award to the University of Utah, focuses on developing advanced computational modeling tools that integrate experimental data, first-principles...
- 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 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...
- 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 $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 electrochemical materials. The research aims to build "smart experimental systems" that can autonomously learn from previous results and optimize experiments to drive breakthroughs in energy storage and information technologies. This work also involves sharing tools and training with students and researchers to build an innovation-driven workforce for advanced industries. The award was made to the University of Tennessee, which will execute the research through its Agriculture Research Division.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $300.8k | 7/16/25 |