Charting a course towards new light-activated molecules

Many small molecules absorb light. Predicting what happens to them next is a challenging task for computer simulations, but if we could solve this problem we would have a new route to designing new fluorophores for medical diagnostic imaging, new photocatalysts for green synthesis, or new chromophores for harvesting the sun's energy in solar cells. In this project, we aim to address this challenge by combining excited-state electronic structure calculations and machine-learning to build new predictive models to help us search the entire space of small organic molecules to identify useful (and previously-unknown) light-activated molecules.