Project Grant 2610933

The National Science Foundation Directorate for Mathematical and Physical Sciences awarded $507,829 to the Board of Regents of Nevada System of Higher Education, operating as University of Nevada, Reno, on August 1, 2026, under the Mathematical and Physical Sciences program (CFDA 47.049) to develop computational methods and machine learning tools for analyzing energy flow in molecules.

Dr. David Leitner and his research group will create machine learning tools to analyze large computational and experimental datasets to predict the nature and rate of energy flow in molecules. The research encompasses vibrational energy dynamics in aromatic compounds, with applications to optimizing catalysis of chemical reactions involving molecules attached to metal nanoparticles. The team will compute large datasets of vibrational frequencies and anharmonic interactions, then apply machine learning to predict how chemical substitution impacts energy transfer and resonance tuning. The work will also apply machine learning tools to analyze energy transfer rates computed for proteins to predict rates of energy flow during chemical reactions and the impact of mutations on protein function. Graduate and undergraduate students will participate in research spanning molecular science, materials chemistry, and biophysical chemistry, with applications to nanotechnology, drug discovery, and medicine.

Performance of work occurs in Reno, Nevada, with a period of performance from August 1, 2026, through July 31, 2029.