This National Science Foundation Project Grant of $418,678 awarded on August 15, 2022 will support the development of computational tools to model complex chemical reactions through dimensionality reduction and machine learning techniques. Under the federal Mathematical and Physical Sciences program (CFDA 47.049), Dmitrij Rappoport of the University of California, Irvine will create low-dimensional representations of potential energy surfaces using nonlinear dimensionality reduction and...
This $578,793 federal Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to revolutionize chemical synthesis and material discovery through an innovative physics-informed machine learning-based approach. The primary objective is to develop an integrated computational framework that combines first-principles modeling, machine learning, and process optimization to enhance the synthesis of perovskite oxides - critical materials for energy...
This $250,000 National Science Foundation project grant will support research at the University of Massachusetts Boston and the University of California-Santa Barbara to advance machine learning techniques for predicting the behavior of dynamic materials. Jason Green of UMass Boston and Igor Mezic of UC Santa Barbara will combine machine learning and physical theory methods to create new approaches for designing functional materials with tailored optical, mechanical, or photonic properties on...
This Project Grant award, valued at $220,991, was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The funding supports theoretical and computational research and education at the University of California, Irvine (UC Irvine) to enhance the accuracy and efficiency of density functional theory (DFT) simulations for studying the electronic structure of materials at the atomic scale. The key developments include: 1)...
The National Science Foundation Division of Chemistry awarded a $387,874 Project Grant to the University of Chicago to develop new theoretical and computational tools for efficiently estimating long-time molecular thermodynamics and kinetics. Under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, Andrew Ferguson of the University of Chicago will establish novel simulation approaches enabled by machine learning and new mathematical theorems to simulate biomolecules...
This $296,555 Project Grant from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support the development of reduced basis enhancements for neural networks and their application to quantum materials simulation. Specifically, the University of Massachusetts Dartmouth will combine traditional reduced basis methods with deep learning techniques to build an analysis-driven computational emulator for parameterized partial differential equations....
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...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $133,476 to Stanford University to develop machine learning models that can efficiently predict and analyze the fluctuations of biomolecules, such as proteins. The project aims to create scalable and transferable generative models that can construct accurate configurational ensembles of diverse molecular systems at a lower computational cost than traditional...
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 $618,912 five-year federal Project Grant awarded by the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports Harvey Mudd College's development of a hybrid adaptive particle-field simulation method to efficiently model large molecules in solution. The new simulation technique aims to greatly reduce the computational cost of all-atom simulations for systems like polymers and protein assemblies, providing a more accessible tool for chemists...