Photochemical reactions exemplify “green” chemistry and are an essential tool for synthesizing highly strained molecules under mild conditions with light. The a priori design of photochemical reactions is challenging because degenerate excited states often result in competing reaction mechanisms to undesired products. Further, a lack of experimental techniques that provide atomistic structural information on ultrafast timescales (10–15 – 10–12 s) limits general ‘chemical intuition’ about these processes. Computations, however, provide a path forward. I will discuss how my group has leveraged state-of-the-art quantum mechanical calculations, non-adiabatic molecular dynamics, and machine learning (ML) techniques to develop the Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD). PyRAI2MD enables 1,000-fold longer simulations than are currently possible with multiconfigurational NAMD simulations. I will describe several applications of this technique for photochemical pericyclic reactions

Open-access software towards facile prediction of photophysical properties and photochemical reactions

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

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