This Project Grant award, valued at $133,476.00, was provided by the National Science Foundation (NSF) Division of Chemistry under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports Grant Rotskoff of Stanford University in developing machine learning models to predict and analyze the fluctuations of biomolecules, such as proteins. The overarching goal is to construct accurate configurational ensembles of diverse molecular systems at a lower computational cost than classical molecular dynamics, by integrating neural networks for density estimation with coarse-grained models. The project aims to enable faster and more efficient studies of complex biological molecules, including those that lack a stable folded structure. In addition, the PI plans to create educational materials about these new computational methods, including a new undergraduate course at Stanford and online resources for high school students and teachers.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $133.5k | 12/26/24 |