RUI: Harnessing Machine Learning Techniques for Atomic and Molecular Collisions — NSF Award to Board of Trustees of Illinois State
Charged particle scattering processes play a vital role in many fields, including plasma physics, astrophysics, and biomedical physics. In order to advance technologies and develop innovative new techniques in these fields, fundamental charged particle collision data is required for electron and heavy-ion scattering. T
| Award title | RUI: Harnessing Machine Learning Techniques for Atomic and Molecular Collisions |
|---|---|
| Award ID | 2512304 |
| Awardee | Board of Trustees of Illinois State University |
| City | NORMAL |
| State | IL |
| Amount obligated | $234,795 |
| Principal investigator | Allison Harris |
| Program | AMO Theory/Atomic, Molecular & |
| Start date | 09/01/2025 |
| Abstract | Charged particle scattering processes play a vital role in many fields, including plasma physics, astrophysics, and biomedical physics. In order to advance technologies and develop innovative new techniques in these fields, fundamental charged particle collision data is required for electron and heavy-ion scattering. This collision data often serves as inputs for sophisticated application-based models, and is needed for a wide range of energies, projectiles, target species, and collision process |
| Source | NSF Awards |
$799/mo
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