Exploring reaction dynamics involving post-transition state bifurcations based on quantum mechanical ambimodal transition states

Abstract Computational methods for predicting product ratios in dynamically controlled reactions with shallow intermediates or bifurcating pathways after an ambimodal transition state are reviewed and benchmarked. The range of methods includes molecular dynamics simulations, machine learning-based models and recent advancements in correlational methods, all of which rely on quantum mechanical computations. Together, these approaches form a computational toolbox that enhances the efficiency and effectiveness of exploring reaction selectivity influenced by dynamic effects.