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 the loss of N2 and the 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) exotype 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 the 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 and 95% accuracy at later reaction time steps. This increased accuracy allowed the feature importance analysis to reveal that, at the later-time analysis, the methylene bridge out-of-plane bending is correlated with trajectory outcomes for the formation of either the exo product or toward 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, the finite but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined this 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.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.

Catalysis and chemical mechanisms of calcite dissolution in seawater.

Near-equilibrium calcite dissolution in seawater contributes significantly to the regulation of atmospheric [Formula: see text] on 1,000-y timescales. Despite many studies on far-from-equilibrium dissolution, little is known about the detailed mechanisms responsible for calcite dissolution in seawater. In this paper, we dissolve 13C-labeled calcites in natural seawater. We show that the time-evolving enrichment of [Formula: see text] in solution is a direct measure of both dissolution and precipitation reactions across a large range of saturation states. Secondary Ion Mass Spectrometer profiles into the 13C-labeled solids confirm the presence of precipitated material even in undersaturated conditions. The close balance of precipitation and dissolution near equilibrium can alter the chemical composition of calcite deeper than one monolayer into the crystal. This balance of dissolution-precipitation shifts significantly toward a dissolution-dominated mechanism below about [Formula: see text] Finally, we show that the enzyme carbonic anhydrase (CA) increases the dissolution rate across all saturation states, and the effect is most pronounced close to equilibrium. This finding suggests that the rate of hydration of [Formula: see text] is a rate-limiting step for calcite dissolution in seawater. We then interpret our dissolution data in a framework that incorporates both solution chemistry and geometric constraints on the calcite solid. Near equilibrium, this framework demonstrates a lowered free energy barrier at the solid-solution interface in the presence of CA. This framework also indicates a significant change in dissolution mechanism at [Formula: see text], which we interpret as the onset of homogeneous etch pit nucleation.