Project Grant DESC0025345

The National Science Foundation Division of Chemistry has awarded a 3-year, $390,000 Project Grant to the University of Georgia Research Foundation to develop new machine learning tools that facilitate the identification of molecular structure from vacuum ultraviolet (VUV) spectroscopy data. The project, titled "Machine Learning Models for Interpreting Molecular Structure from Vacuum Ultraviolet Spectra", aims to create ML models capable of over 95% accuracy in predicting key details...

This Project Grant award, funded by the National Science Foundation (NSF) Division of Chemistry under the Mathematical and Physical Sciences grant program (CFDA 47.049), supports Professor Abigail G. Doyle at the University of California, Los Angeles (UCLA) in developing novel synthetic reactions that use visible light and photocatalysts to access high-energy, polar reactive intermediates. The $630,000 award will fund the development of several key transformations, including a desaturation...

The National Science Foundation awarded a $299,569 EAGER (EArly-concept Grants for Exploratory Research) Project Grant to the University of Rochester to develop an approach that leverages large language models and artificial intelligence to accelerate the discovery of earth-abundant, active, and selective catalysts for the reverse water-gas shift reaction. The project aims to demonstrate that language-based representations can be universally applied to materials discovery processes expressed...

This Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), supports Professor Stefan Bernhard of Carnegie Mellon University in developing data-driven, automated methodologies to study and control light-driven catalytic processes. The $575,000 award, spanning September 2024 to August 2027, aims to advance sustainable chemistry through new approaches to photocatalytic hydrogen generation from bio-based feedstocks like...

This collaborative research project, funded by the National Science Foundation (NSF) Division of Chemistry under the Mathematical and Physical Sciences program (CFDA 47.049), supports a three-year investigation into photocatalytic activity descriptors of atomically dispersed cobalt sites on graphitic carbon nitride. Awarded on September 1, 2025, with $300,000 in total federal obligations through August 31, 2028, the project is led by Professor Gonghu Li (University of New Hampshire), Professor...

This $431,430 National Science Foundation project grant supports the development of machine learning-aided methods to discover synthesizable, active, and stable heterogeneous catalyst materials. Funded under the NSF Engineering program (CFDA 47.041), the three-year award to the University of Michigan involves collaborating with Wayne State University to create an open-source, computer-aided workflow and tools for predicting catalyst properties beyond just activity. The project will apply density...

The National Science Foundation Division of Chemistry awarded a $650,000 Project Grant to the Massachusetts Institute of Technology from March 1, 2022 to February 28, 2027. The grant funds the development of novel domain-tailored machine learning tools for organic reaction development and discovery under the Mathematical and Physical Sciences program (CFDA 47.049). The grant supports research to increase the store of scientific knowledge and enhance understanding of major problems confronting...

This $431,454 Project Grant award from the National Science Foundation's (NSF) Division of Chemistry was provided to The Johns Hopkins University and the University of Massachusetts, Lowell to study the design of catalytic materials for electrochemical synthesis of organonitrogen compounds. The overarching goal is to develop a fundamental understanding of electrochemical carbon-nitrogen coupling using metal-organic frameworks called boron imidazolate frameworks (BIFs) as the catalyst platform....

This three-year, $575,000 National Science Foundation project grant supports research at the University of North Carolina at Chapel Hill toward molecular control of cage escape yields in bimolecular photochemistry. Professor Gerald Meyer and his team will study photo-initiated electron transfer reactions between photosensitizers and quenchers to gain mechanistic insights that allow optimization and prediction of quantum yields. A focus on excited state electron transfer and charge...

ACED: Accelerating Materials Discovery by Learning with Physics-Informed Constraints The University of Minnesota received a $500,000 Project Grant award from the National Science Foundation's Division of Information and Intelligent Systems (Computer and Information Science and Engineering program, CFDA 47.070) to develop advanced machine and deep learning (MDL) models for accelerated materials discovery. The project, awarded June 15, 2025, with completion targeted for November 30, 2026, aims...