Project Grant 2331939

Award Date 8/1/24
Completion Date 7/31/27
Dollars Obligated $557K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Boulder, CO 80309, USA
Similar Awards
This $165,123 project grant from the National Science Foundation's Mathematical and Physical Sciences program will support the development of new computational methods and software tools for predicting polymorphic crystal structures of organic molecules and elucidating phase transition mechanisms. Mark Tuckerman of New York University and Jerome Delhommelle of the University of North Dakota will create a topological theory for crystal structure generation based solely on molecular order...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $273,291 to the University of California, Santa Barbara to develop computational tools that combine machine learning and scientific computing for the exploration and prediction of polymer systems. The goal is to accelerate the discovery of new materials and provide a framework for computationally costly problems across various scientific domains. The research...
This Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will fund a collaborative research project at Northeastern University to develop computer simulations that study how organic molecules form crystals when mixed with a liquid and placed inside materials with tiny nanometer-scale pores. The $278,077 award, effective July 1, 2025 through June 30, 2028, aims to improve understanding of how confinement in nanopores affects the nucleation and...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports computational and theoretical research at New York University (NYU) aimed at quantifying the complexity of materials landscapes and advancing materials discovery. The $280,000 award, which runs from March 1, 2025 to February 28, 2030, will fund the development of methods to efficiently sample complex energy landscapes, predict non-equilibrium entropies, and...
This Project Grant award from the National Science Foundation (NSF) Chemical Theory, Models and Computational Methods program (CFDA 47.049 - Mathematical and Physical Sciences) supports Stanford University's research to develop scalable and transferable machine learning models for predicting and analyzing the conformational fluctuations of biomolecules like proteins. The $699,487 award seeks to construct quantitatively accurate configurational ensembles of diverse molecular systems at lower...
This Project Grant awarded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041) will create computer simulations to study how organic molecules form crystals when mixed with a liquid and placed inside materials with tiny holes (nanopores). The $312,589 award to North Carolina State University, with a project period from Jul 1, 2025 to Jun 30, 2028, aims to understand how confinement in nanopores affects the nucleation and formation of crystal polymorphs. The...
The National Science Foundation Directorate for Mathematical and Physical Sciences awarded a $647,165 Project Grant to the University of Colorado Boulder to support research titled "CAREER: HIGH ACCURACY METHODS FOR ELECTRONIC STRUCTURE OF MOLECULES AND MATERIALS" from February 1, 2022 through January 31, 2027. The grant supports the Mathematical and Physical Sciences program (CFDA 47.049), which aims to promote progress in these scientific fields and strengthen the nation's scientific...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) provides $299,300.00 to Carnegie Mellon University to advance the fundamental understanding and control of crystal growth processes. The researchers are developing new in-situ monitoring techniques and machine learning models to optimize the flux growth synthesis of heavy fermion quaternary perovskite compounds. This work aims to improve the reliability and...
This $254,856 federal Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports data-intensive and computational research and education at the University of Illinois. The project aims to develop a new machine learning framework for the inverse design of sequence-defined macromolecules that can self-assemble into targeted morphologies and properties. The research will leverage generative deep learning models to predict the...
The National Science Foundation (NSF) Division of Chemistry awarded a $596,090 project grant to the University of Iowa for the development of new computational methods to simulate and predict the physical properties of complex organic crystals. This 3-year grant, effective July 1, 2025, supports research led by Dr. Michael Schnieders to create faster techniques for determining the most stable crystal polymorph and integrate acid/base chemistry into molecular models. These innovations could...

This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $556,697 to the University of Colorado Boulder to improve approaches for predicting molecular crystal properties using generative modeling techniques. The project, led by Professor Michael Shirts, aims to develop machine learning models that can accurately explore molecular conformational ensembles and estimate the thermodynamics of small molecule crystals. This research is vital for advancing pharmaceutical development, as most drugs are distributed in crystalline form. The project will also contribute open-source software, tutorials, and educational resources to support the broader computational molecular science community. The award period runs from August 1, 2024, to July 31, 2027.

Generated 5/13/25, 2:14 AM