Revealing the origins of kinetic selectivity is one of the premier tasks of applied theoretical organic chemistry, and for many reactions, doing so involves comparing competing transition states. For some reactions, however, a single transition state leads directly to multiple products, in which case non-statistical dynamic effects influence selectivity control. The selectivity of photochemical reactions—where crossing between excited-state and ground-state surfaces occurs near ground-state transition structures that interconvert competing products—also should be controlled by the momentum of the reacting molecules as they return to the ground state in addition to the shape of the potential energy surfaces involved. Now, using machine-learning-assisted non-adiabatic molecular dynamics and multiconfiguration pair-density functional theory, these factors are examined for a classic photochemical reaction—the deazetization of 2,3-diazabicyclo[2.2.2]oct-2-ene—for which we demonstrate that momentum dominates the selectivity for hexadiene versus [2.2.2] bicyclohexane products.
Selectivity of photochemical reactions is notoriously difficult to model. Now it has been shown that by employing an analogy to ground-state reactions with post-transition-state bifurcations, selectivity for a complex photochemical denitrogenation reaction can be captured and rationalized, and its dynamical origins understood.