Abstract: 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 complete active space self consistent field (CASSCF) calculations, non-adiabatic molecular dynamics, and machine learning (ML) techniques to understand the reactivities and selectivities of a photochemical pericyclic reactions, including a light-driven 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 100,000-fold longer simulations than are currently possible with multiconfigurational NAMD simulations. PyRAI2MD has enabled the first nanosecond ML-NAMD simulations on stereoselective electrocyclic reactions with record degrees of freedom and molecular complexities. Future directions involving the simulation of photochemical reactions in complex solvated and crystalline environments will be presented.
Originally published at chemistry.nd.edu.