Our research is diverse by design – the unifying theme is the careful methodological approach that we employ. For instance, we utilize highly correlated multireference quantum chemical techniques to study the structures and energies of diradicals, and correlated, single reference methods to study halogen bonding and reaction pathways for pyrolysis and combustion. We also utilize efficient sampling methods to study conformational flexibility, host-guest interactions and protein folding.  Most recently we have started utilizing machine learning to predict molecular properties from quantum mechanical data. A variety of projects will be presented including the computational optimization of an explosive sensor.

Detecting explosives, designing cancer drugs and using machine learning to predict molecular properties

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.