Machine learning classification of disrotatory IRC and conrotatory non-IRC trajectory motion for cyclopropyl radical ring opening

Vibrational synchronization and its reaction pathway influence from an entropic intermediate in a dirhodium catalyzed allylic C-H activation/Cope rearrangement reaction

In reactions with consecutive transition states without an intermediate, and an energy surface bifurcation, atomic motion generally determines product selectivity. Understanding this dynamic-based selectivity can be straightforward if there is extremely fast descent from the first transition state to a product. However, in cases where a nonstatistical roaming/entropic intermediate occurs prior to product formation the motion that influences selectivity can be difficult to identify. Here we report quasiclassical direct dynamics trajectories for the dirhodium catalyzed reaction between styryldiazoacetate and 1,4-cyclohexadiene and prior experiments by Davies showed competitive allylic C-H insertion and Cope products. Trajectories confirmed the proposed energy surface bifurcation and revealed that dirhodium vinylcarbenoid when reacting with 1,4-cyclohexadiene can induce either a dynamically concerted pathway or a dynamically stepwise pathway with a nonstatistical entropic tight ion-pair intermediate. In the dynamically stepwise reaction pathway C-H insertion versus Cope selectivity is highly influenced by whether or not vibrational synchronization occurs in the nonstatistical entropic intermediate. This vibrational synchronization highlights the possible need for an entropic intermediate to have organized transition state-like motion to proceed to a product.

Dynamical Origin of Rebound versus Dissociation Selectivity during Fe-Oxo-Mediated C-H Functionalization Reactions

The mechanism of catalytic C-H functionalization of alkanes by Fe-oxo complexes is often suggested to involve a hydrogen atom transfer (HAT) step with the formation of a radical-pair intermediate followed by diverging pathways for radical rebound, dissociation, or desaturation. Recently, we showed that in some Fe-oxo reactions, the radical pair is a nonstatistical-type intermediate and dynamic effects control rebound versus dissociation pathway selectivity. However, the effect of the solvent cage on the stability and lifetime of the radical-pair intermediate has never been analyzed. Moreover, because of the extreme complexity of motion that occurs during dynamics trajectories, the underlying physical origin of pathway selectivity has not yet been determined. For the reaction between [(TQA_Cl)FeIVO]+ and cyclohexane, here, we report explicit solvent trajectories and machine learning analysis on transition-state sampled features (e.g., vibrational, velocity, and geometric) that identified the transferring hydrogen atom kinetic energy as the most important factor controlling rebound versus nonrebound dynamics trajectories, which provides an explanation for our previously proposed dynamic matching effect in fast rebound trajectories that bypass the radical-pair intermediate. Manual control of the reaction trajectories confirmed the importance of this feature and provides a mechanism to enhance or diminish selectivity for the rebound pathway. This led to a general catalyst design principle and proof-of-principle catalyst design that showcases how to control rebound versus dissociation reaction pathway selectivity.

Dynamics study of the post-transition-state-bifurcation process of the (HCOOH)H+ → CO + H3O+/HCO+ + H2O dissociation: application of machine-learning techniques

The process of protonated formic acid dissociating from the transition state was studied using ring-polymer molecular dynamics (RPMD), classical MD, and quasi-classical trajectory (QCT) simulations. Temperature had a strong influence on the branching fractions for the HCO⁺ + H₂O and CO + H₃O⁺ dissociation channels. The RPMD and classical MD simulations showed similar behavior, but the QCT dynamics were significantly different owing to the excess energies in the quasi-classical trajectories. Machine-learning analysis identified several key features in the phase information of the vibrational motions at the transition state. We found that the initial configuration and momentum of a hydrogen atom connected to a carbon atom and the shrinking coordinate of the CO bond at the transition state play a role in the dynamics of HCO⁺ + H₂O production.

Prediction of Kinetic Product Ratios: Investigation of a Dynamically Controlled Case

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 outcomes─that is, which product well on the potential energy surface was selected─could 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.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Post-spin crossing dynamics determine the regioselectivity in open-shell singlet biradical recombination

Comprehensive computational studies reveal unique dynamic effects in a multi-spin-state reaction that determine the regioselectivity of a biradical recombination process.