This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) is funding the development of an experimental framework to enhance large language models (LLMs) for generating innovative materials design hypotheses. The $150,843 award, granted on February 1, 2025 with a completion date of January 31, 2027, aims to expand LLMs' capabilities by enabling them to ingest and learn from diverse data formats beyond just...
This $500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) will support the development of an innovative deep learning framework that combines physics-informed principles with scientific domain-adapted generative diffusion models. The goal is to overcome key challenges in scientific inverse design and accelerate scientific discovery, particularly in the field of nanophotonics. The research aims to...
This Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences program (CFDA 47.049) will provide $277,686 to the Regents of the University of Minnesota to develop advanced computational modeling and machine learning workflows for exploring the mechanical and electronic properties of 2D quantum materials. The project aims to enable rapid, automated, high-fidelity simulations of these materials, which are critical for advancing emerging...
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 $100,000 federal Project Grant award was provided by the National Science Foundation's (NSF) Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) under the Engineering (CFDA 47.041) program. The grant supports the development of a Bayesian symmetry-respecting machine learning framework to predict electronic structures for materials design. Key objectives include enhancing uncertainty quantification, preserving symmetries, incorporating physics, and improving accuracy,...
The National Science Foundation (NSF) awarded Arizona State University a 5-year, $196,726 CAREER Program grant under the Computer and Information Science and Engineering (CISE) grant program (CFDA 47.070) to support a research and education program focused on accelerating scientific discovery through physics-informed deep learning models. The project aims to develop physically-consistent dynamics models, deep learning-based symbolic regression algorithms, and multimodal deep learning...
This $198,498 Project Grant awarded by the National Science Foundation (NSF) Division of Materials Research under the Mathematical and Physical Sciences program (CFDA 47.049) supports research at Drexel University aimed at developing a data-driven framework to predict synthesis pathways and optimal conditions for producing computationally-designed solid-state inorganic materials. The project will utilize deep learning, computational thermodynamic modeling, and validation experiments to...
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 EAGER (Early-Concept Grants for Exploratory Research) award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will develop generative artificial intelligence (AI) methods to learn from computational physics simulations and mathematical equations. The $300,000 project aims to expand the capabilities of large language models, such as OpenAI's ChatGPT and Microsoft's Copilot, to go beyond text-based learning and make predictions on complex, coupled physics problems...
This $499,999 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) aims to develop a computational framework that accelerates the prediction and optimization of microstructures in additive manufacturing (AM) processes. The project involves designing novel graph neural network models that incorporate physical laws to enable faster, more accurate, and generalizable microstructure predictions. This...