This Project Grant from the National Science Foundation provides $1,367,545 to the University of Michigan under the Engineering program (CFDA 47.041) from October 1, 2021 to September 30, 2025. The funding supports collaborative research titled "Machine Learning-Aided Discovery of Synthesizable, Active and Stable Heterogeneous Catalysts." The Engineering program seeks to improve quality of life and economic strength through innovation and excellence in engineering research. This...
The National Science Foundation (NSF) awarded a $638,566 Project Grant under the Engineering program (CFDA 47.041) to the University of Delaware. The grant supports a collaborative research effort to develop a computational-experimental methodology using machine learning to design stable, active, and selective single-atom catalysts for industrial applications. The project aims to uncover physics-inspired descriptors to predict how the support material properties influence the stability,...
The National Science Foundation (NSF) awarded a $291,349 Project Grant to the University of Alabama under the Engineering program (CFDA 47.041) to develop and apply advanced machine learning force fields to simulate nanoparticle catalysts under realistic reaction conditions. The goal is to elucidate the catalytic active sites and how nanoparticle shapes evolve during catalytic processes. This research will help enable more sustainable chemical manufacturing by improving the computational...
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...
The National Science Foundation (NSF) Division of Chemistry awarded a $521,959 Project Grant to the Regents of the University of Michigan to develop chemist-in-the-loop machine learning approaches for predicting chemical reaction outcomes, under the Mathematical and Physical Sciences program (CFDA 47.049). The project, led by Professor Paul Zimmerman, will combine advanced machine learning with graph-based reaction discovery tools to overcome data scarcity challenges in reaction mechanism...
This National Science Foundation (NSF) grant under the Engineering (CFDA 47.041) program provides $625,184 to Virginia Polytechnic Institute & State University (Virginia Tech) to develop a design methodology for supported single-atom catalysts (SACs) - an emerging class of catalysts with exciting properties that can revolutionize industrial applications. The project embraces an integrated computational-experimental approach using machine learning to understand how the properties of the...
The National Science Foundation (NSF) awarded a $330,000 Project Grant under the Engineering (CFDA 47.041) program to the Regents of the University of Michigan to conduct collaborative research on engineering catalyst selectivity for the deconstruction and re-synthesis of biomass and waste plastic feedstocks. The research aims to develop a fundamental understanding of how tuning catalyst architecture can enable selective cleavage of carbon-oxygen bonds in multifunctional organic molecules...
This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $569,490 to Michigan Technological University (Michigan Tech) to develop a Bayesian symmetry-respecting machine learning framework for predicting electronic structures in materials design. The research aims to create a machine learning model that can accurately and efficiently predict electron density for a wide range of...
Under a $529,170 award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (MPS) program (CFDA 47.049), Professor Robert Paton and his research team at Colorado State University (CSU) will develop new computational workflows to accelerate the discovery and optimization of highly selective catalysts. The project aims to advance mechanistic understanding and establish new quantitative descriptors for three key catalyst and ligand classes - hydrogen-bond donors,...
This National Science Foundation (NSF) Project Grant award, funded through the Mathematical and Physical Sciences program (CFDA 47.049), provides $424,350 to Duquesne University from September 1, 2023 to August 31, 2026. The project aims to design and discover energy-efficient, environmentally friendly, and cost-effective metal-free catalysts for the activation and conversion of small molecules, such as converting carbon dioxide to useful chemicals and fuels. The research approach combines...
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 functional theory calculations and machine learning screening to rapidly evaluate descriptors of synthesizability, stability, and activity for bimetallic catalyst case studies in carbon monoxide oxidation and ethylene oxide production. Predicted catalyst structures and compositions will be validated experimentally in an iterative feedback loop to accelerate discovery. Broader impacts include cross-disciplinary training and outreach focused on student professional development and broadened participation in science.