Photochemical reactions are increasingly important for the construction of value-added, strained organic architectures. Direct excitation and photoredox reactions typically require mild conditions and permit access highly strained molecules and new synthetic methodologies. 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 understand the reactivities and selectivities of a photochemical cascade reaction towards the first stable polyacetylene, fluoropolyacetylene. I will introduce our new open-access machine learning tool, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD), which enables 1,000-fold longer simulations than are currently possible with multiconfigurational NAMD simulations. PyRAI2MD has enabled nanosecond ML-NAMD simulations on stereoselective electrocyclic reactions with record degrees of freedom and molecular complexities.

Photodynamics simulations explain photochemical reactivity and selectivities towards strained molecules

ACS Spring 2022

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