Project Grant DESC0021599

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 NSF Integrative Activities (CFDA 47.083) project grant, awarded to Iowa State University, provides $276,827 to develop a novel cyberinfrastructure for designing and characterizing printable multifunctional materials. Key products and services being delivered include: A universal, parallelized in silico voxelized microstructure generator to produce datasets of material morphologies with distinct properties and manufacturability Numerical methods for efficient characterization of...

ACED: 3D ConvNets for Discovery of Nanoporous Materials The University of Massachusetts Amherst received a $500,000 Project Grant from the National Science Foundation's (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering (CISE) program (CFDA 47.070) for the period August 1, 2025 through July 31, 2027. The project will develop advanced machine learning tools, specifically three-dimensional convolutional neural networks (3D ConvNets), to...

The National Science Foundation Division of Materials Research awarded $320,000 under the Mathematical and Physical Sciences program (CFDA 47.049) to the Massachusetts Institute of Technology for a collaborative research project on combinatorial solution processing of optical phase change materials. The researchers will develop a high-throughput synthesis and characterization paradigm for accelerated discovery and customization of multi-component phase change alloy thin films tailored for...

This $1,425,000 Project Grant awarded by the National Science Foundation's Division of Materials Research (DMR) under the Designing Materials to Revolutionize and Engineer Our Future (DMREF) program (CFDA 47.049) supports collaborative research to develop programmable soft materials design and synthesis capabilities. The research aims to integrate molecular design codes, automated synthesis, and structural analysis to create an artificial intelligence-driven materials design platform with...

The National Science Foundation (NSF) Division of Materials Research awarded a $200,000 EAGER (Early-concept Grants for Exploratory Research) project grant to the University of Florida (UF) Division of Sponsored Research. The grant, titled "SSMCDAT2023: Database Generation to Identify Trends in Inter- and Intra-Polyhedral Connectivity and Energy Storage Behavior", supports research and education activities focused on understanding the relationships between the composition, atomic...

The National Science Foundation (NSF) awarded a $75,000 Project Grant under the Engineering (CFDA 47.041) program to the University of California, Santa Barbara (UCSB) to support a collaborative research project titled "DMREF: Data-Driven Discovery of the Processing Genome for Heterogeneous Superalloy Microstructures." The project aims to revolutionize the creation of novel engineering alloys by developing a data-driven platform called "DRAGONS" (Data-Driven Recursive...

This $1,481,906 Project Grant award from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) supports research to discover and develop new multi-principal-element hexagonal-close-packed structural alloys using advanced computational materials science and additive manufacturing methods. The University of California, Berkeley is the awardee, with the State of California Controllers Office serving as the parent organization. The project aims to rapidly...

This Project Grant award from the National Science Foundation (NSF) Office of Advanced Cyberinfrastructure under the Computer and Information Science and Engineering (CFDA 47.070) program supports the development of an open-source high-fidelity materials database using the DFT+DMFT method. The $149,983 award to Rutgers, The State University, aims to overcome the limitations of existing materials databases built using basic Density Functional Theory (DFT) by incorporating the more precise...

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...