Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene

Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene (1) results in N2 loss and formation of bicyclo products 3 (exo) and 4 (endo) in a nonstatistical ratio with preference for the exo product. Here we report unrestricted M06-2X quasiclassical trajectories initialized from the concerted N2 ejection transition state that were able to replicate the experimental preference to form 3. We found that the 3:4 ratio results from the relative amounts of very fast (ballistic) exo-type trajectories versus trajectories that lead to the 1,3-diradical intermediate 2. These quasiclassical trajectories provided a set of transition-state vibrational, velocity, momenta, and geometric features for machine learning analysis. A selection of popular supervised classification algorithms (e.g. Random Forest) provided poor prediction of trajectory outcomes based on only transition-state vibrational quanta and energy features. However, these machine learning models provided more accurate predictions using atomic velocities and atomic positions, attaining ~70% accuracy using initial conditions, and between 85-95% accuracy at later reaction time steps. This increased accuracy allowed feature importance analysis to reveal that at the later time analysis the methylene bridge out-of-plane bending is correlated with trajectory outcomes as either formation of the exo product or towards the diradical intermediate. Possible reasons for the struggle of machine learning algorithms to classify trajectories based on transition-state features is the heavily overlapping feature values, finite, but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined chaos by comparing a set of nearly identical trajectories that differed by only a very small scaling of the kinetic energies resulting from the transition-state reaction coordinate.