This Project Grant award of $500,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program will support the development of new machine learning tools capable of rapidly predicting structure-performance relationships for nanoporous materials. The research, conducted by the University of Massachusetts, aims to accelerate the discovery of nanoporous materials for applications in clean energy and sustainability, such as gas storage, membrane separations, solid-state batteries, and plastic waste upcycling. The project will leverage advanced 3D convolutional neural networks to efficiently capture the precise positioning of atoms in 3D space, which is critical for understanding confinement effects in nanoporous materials. The research outcomes may lead to a general representation framework that can accurately describe subtle noncovalent interactions in extended 3D materials, benefiting a range of material systems. This award, with a performance period from August 1, 2025 to July 31, 2027, reflects NSF's mission to support fundamental and applied research in computing and information science.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $500.0k | 7/9/25 |