Project Grant 2401663
Award Date
6/1/24
Completion Date
5/31/27
Dollars Obligated
$179K
Federal Agency
Division of Materials Research
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Penn State University, PA 16802, USA
Similar Awards
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...
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 $300,000 Project Grant from the National Science Foundation's Division of Mathematical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of next-generation mathematical and algorithmic tools to address two key issues in applying machine learning to statistical modeling of time-evolving complex systems: a shortage of informative training data and the high computational costs of high-dimensional problems. Specifically, the...
This National Science Foundation Project Grant of $728,684 supports research at The Pennsylvania State University to develop new machine learning and mathematical methods for quality control in manufacturing. Funded under the Engineering program (CFDA 47.041), the award runs from June 1, 2022 to May 31, 2025. Specifically, the university will first create improved algorithms for identifying a subset of key process variables in continuous manufacturing that best indicate overall plant control,...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $244,960 to the Regents of the University of Minnesota to develop computational techniques for discovering new block polymer materials. The project aims to overcome limitations of current theory-driven approaches by using generative artificial intelligence and machine learning to propose and evaluate novel block polymer structures. This computational workflow...
This Project Grant award of $340,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) supports fundamental research on polymer nanocomposites. The research aims to: Elucidate how the charge density of polymer brushes affects the dispersion of nanoparticles in polyelectrolyte matrices and explore pathways to produce percolated structures. Investigate the infiltration kinetics of polar polymers into bicontinuous scaffolds, which could enable...
The National Science Foundation (NSF) Division of Chemistry awarded a $575,329 Project Grant to The Pennsylvania State University (Penn State) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049). The grant supports William Noid and his research group in developing improved methods for coarse-grained (CG) models of polymers and peptides. This work aims to enhance the accuracy and transferability of CG models, which provide computational efficiency for investigating...
This Project Grant award, funded by the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049), supports the development of machine learning models to predict and analyze the fluctuations of biomolecules, such as proteins. The award of $133,476 to Stanford University, led by Principal Investigator Grant Rotskoff, aims to create scalable and transferable models for sampling conformational ensembles of biomolecules by integrating neural networks with...
This $210,335 NSF Mathematical and Physical Sciences (CFDA 47.049) Project Grant award to Drexel University supports the development of a new computational method to accelerate the design of new polymeric materials. The project aims to create a simulation technique that can simultaneously resolve both monomer-level chemistry and mesoscopic length scales within polymeric materials. This has the potential to significantly speed up the development of new polymers for diverse applications like...
This Project Grant from the National Science Foundation's Division of Materials Research, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $361,129 to Princeton University from January 2023 through December 2027. The funding will support the development of predictive tools to facilitate the understanding and design of stimuli-responsive polymers through multiscale molecular modeling and machine learning techniques. Specifically, the principal investigator will work to...