Project Grant 2453727
- 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 (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,...
- This federal Project Grant award of $212,882 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project between Johns Hopkins University and the University of Massachusetts, Lowell. The project, led by Professors V. Sara Thoi and Fanglin Che, aims to design catalytic materials for the electrochemical synthesis of commercially valuable organonitrogen compounds from abundant carbon and nitrogen sources. The...
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with the CFDA number 47.070, is focused on developing new machine learning tools to rapidly predict structure-performance relationships for nanoporous materials. The $500,000 award, granted from Aug 1, 2025 to Jul 31, 2027, aims to accelerate the discovery of nanoporous materials for applications in clean energy and sustainability, such as gas storage, membrane...
- This $200,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to expand the library of potential zeolite materials for sustainable catalysis applications. The award recipient, San Jose State University Research Foundation, will investigate the use of non-traditional chemical compositions in zeolites to create unique structural features beyond what can be achieved through traditional synthetic methods. Specifically, the project will study the role...
- 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 Chemical, Bioengineering, Environmental, and Transport Systems awarded a $597,273 Project Grant to the Rector & Visitors of the University of Virginia to develop computational molecular models and theory predicting the dynamics of agglomeration and redispersion of metals supported by zeolites. This project, funded under the NSF Engineering program (CFDA 47.041), will provide guidance on engineering deactivation resistant zeolites used widely in the...
- This federal Project Grant award of $344,016 from the National Science Foundation's Engineering program (CFDA 47.041) supports collaborative research to improve understanding of catalytic reactions for converting hydrogen and carbon-containing feedstocks, such as CO and CO2, into synthetic fuels. The research focuses on two benchmark systems - methanol synthesis on Cu-based catalysts and methanation on Ni-based catalysts - to examine the mechanistic role of densely covered catalyst surfaces in...
- This National Science Foundation project grant of $728,692 supports research at the University of California, Los Angeles from September 2022 through August 2025 under the Engineering program (CFDA 47.041). The university will develop machine learning methods to model the dynamic structures of platinum and nickel nanoparticles containing 20 to 200 atoms and their effects on dehydrogenation and hydrogenolysis reactions. Researchers will generate accurate interatomic potentials using neural...
- This National Science Foundation (NSF) Project Grant under CFDA Program 47.041 - Engineering, awarded to The Johns Hopkins University, aims to develop a new type of zeolite membrane for efficient gas separation in chemical manufacturing. The $425,000 grant, awarded from June 2025 to May 2029, will investigate a scalable approach for synthesizing selective zeolite membranes on porous supports. The project will focus on using zeolite nanosheets as building blocks to create gas-selective...
This federal Project Grant award of $325,000, provided by the National Science Foundation's Engineering program (CFDA 47.041), supports a collaborative research project between researchers at The Johns Hopkins University. The project aims to develop computational approaches for examining metal clusters within the nanometer-scale pores of aluminosilicate materials called zeolites, in order to understand how the confinement of these metal nanoclusters impacts their catalytic reactivity. By combining computer simulations and machine learning, the researchers seek to design materials that can perform chemical reactions, particularly those involving hydrogen storage and transport, with lower energy inputs. The project will also provide educational and outreach opportunities to train students in new machine learning techniques and engage the public through catalysis simulation tools and applications.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $325.0k | 8/28/25 |