The National Science Foundation Division of Chemistry has awarded a 3-year, $390,000 Project Grant to the University of Georgia Research Foundation to develop new machine learning tools that facilitate the identification of molecular structure from vacuum ultraviolet (VUV) spectroscopy data. The project, titled "Machine Learning Models for Interpreting Molecular Structure from Vacuum Ultraviolet Spectra", aims to create ML models capable of over 95% accuracy in predicting key details of molecular structure, particularly for elusive chemical species important in fields like photochemistry, combustion, and atmospheric chemistry. This effort leverages isomer-resolved VUV spectroscopy measurements to train deep neural networks, boosted decision trees, and support vector machines to recognize functional groups and other molecular motifs, enabling predictive analysis of complex chemical species. The resulting insights are expected to support innovations in sustainable energy technologies and improve the understanding of fundamental chemical processes.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $390.0k | 8/14/23 |