Of the various factors influencing kinetically controlled
product
ratios, the role of nonstatistical dynamics is arguably the least
well understood. In this paper, reactions were chosen in which dynamics
played a dominant role in product selection, by design. Specifically,
the reactions studied were the ring openings of cyclopropylidene to
allene and tetramethylcyclopropylidene to tetramethylallene (2,4-dimethylpenta-2,3-diene).
Both reactions have intrinsic reaction coordinates that bifurcate
symmetrically, leading to products that are enantiomeric once the
atoms are uniquely labeled. The question addressed in the study was
whether the outcomesthat is, which product well on the potential
energy surface was selectedcould be predicted from their initial
conditions for individual trajectories in quasiclassical dynamics
simulations. Hybrid potentials were developed based on cooperative
interaction between molecular mechanics and artificial neural networks,
trained against data from electronic structure calculations. These
potentials allowed simulations of both gas-phase and condensed-phase
reactions. The outcome was that, for both reactions, prediction of
initial selection of product wells could be made with >95% success
from initial conditions of the trajectories in the gas phase. However,
when trajectories were run for longer, looking for “final”
products for each trajectory, the predictability dropped off dramatically.
In the gas-phase simulations, this drop off was caused by trajectories
hopping between product wells on the potential energy surface. That
behavior could be suppressed in condensed phases, but then new uncertainty
was introduced because the intermolecular interactions between solute
and bath, necessary to permit intermolecular energy transfer and cooling
of the hot initial products, often led to perturbations of the initial
directions of trajectories on the potential energy surface. It would
consequently appear that a general ability to predict outcomes for
reactions in which nonstatistical dynamics dominate remains a challenge
even in the age of sophisticated machine-learning capabilities.