This National Science Foundation Project Grant of $199,778 will support the development of new machine learning-enabled nanoinformatics tools and systems to advance nanomaterial design through 2026. Funded under the Computer and Information Science and Engineering program (CFDA 47.070), this award to the Regents of the University of Minnesota will deliver a novel machine learning-based nanoinformatics framework. This framework will integrate new digital representations of nanostructures with...
This federal Project Grant award was provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070). The primary objective of this $245,916 award to the University of Maryland, College Park is to develop a novel machine learning-based nanoinformatics framework to address computational challenges in large-scale nanomaterial data mining and analysis. Key research goals include automating nanostructure digitalization,...
This National Science Foundation (NSF) Engineering (CFDA 47.041) project grant award of $269,542, effective March 1, 2025 through February 29, 2028, supports a collaborative research project led by Rutgers, The State University to develop a data science-enabled approach for learning, predicting, and simulating the behavior of nanoparticle populations in advanced nanomanufacturing processes. The project aims to combine numerical simulations with in-situ environmental transmission electron...
This federal Project Grant award, funded by the National Science Foundation's Engineering program (CFDA 47.041), provides $280,405 to the University of Pittsburgh to conduct research aimed at developing a data science-based framework to enable learning, predicting, and simulating nanoscale fabrication processes. Key objectives include: Conducting in-situ environmental transmission electron microscopy (E-TEM) measurements to elucidate the dynamics and spatial proximity effects of metal...
This $450,000 Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) aims to understand and control the interfacial dynamics governing the formation of discrete tiles of nanoparticle assemblies on functionalized fluid interfaces. The research combines experimental and computational methods to investigate and manipulate the thermodynamic and kinetic factors influencing nanoparticle assembly at fluid interfaces. The goal is to achieve precise control...
This $199,040 Project Grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop new classes of computational algorithms that combine the benefits of direct computer simulations and the speed of machine learning predictions. The project, titled "XTRIPODS: HYBRID SCIENCE-MACHINE LEARNING SOLVERS FOR NANOPHOTONICS AND METAMATERIALS," will embed scientific knowledge into the machine learning...
This Project Grant award from the National Science Foundation's Division of Chemical, Bioengineering, Environmental, and Transport Systems (CFDA 47.041 - Engineering) provides $154,577 to Duke University to develop machine learning algorithms for predicting protein-nanoparticle interactions. The research team will characterize protein adsorption on nanoparticles with varying properties and train machine learning models to forecast protein corona formation. The project aims to promote the safe...
The National Science Foundation Division of Information and Intelligent Systems awarded a $180,000 two-year Project Grant to Tulane University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support Tulane University's research project "EAGER: Scalable, Content-Based, Domain-Agnostic Search of Scientific Data Through Concise Topological Representations." The project aims to develop techniques for searching large scientific datasets in...
This National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) Project Grant award to The Administrators of the Tulane Educational Fund (Tulane University) provides $112,576 in funding from February 1, 2024 to March 31, 2024 to support theoretical research, computation, and education aimed at developing more accurate and predictive density functionals for the exchange-correlation energy in quantum mechanical modeling of molecules, chemicals, and materials. The research...
The National Science Foundation (NSF) awarded a $343,766 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to Purdue University. The grant supports Dr. Jianing Li's research to develop data-driven, hierarchical computational modeling methods to predict the properties of biocompatible, bioactive nanomaterials for applications in sensing, drug delivery, and tissue engineering. The project aims to create systematic, adaptable approaches to coarse-grain molecular...