We developed a sampling strategy for rare events to generate chemical reaction trajectories using which a statistical investigation can be performed on the thermodynamics, kinetics, dynamics and mechanisms of the chemical reactions in solution. Free of predefined CVs or RCs, the post-analysis of reaction mechanisms can be performed. As an example, the reaction coordinate(s) of a (retro-)Claisen rearrangement in bulk water was variationally optimized based on a Bayesian learning algorithm as well as a machine learning method. It was found that the hydrogen-bonding of water molecule to the charge-enriched site of the reactant state changes the dynamics of reaction. A characteristic shrinkage of the solvent shell during the chemical transition was identified, suggesting a necessary energy transferring process during the thermal activated chemical reaction, reminiscent of the “cage-effect”. We have also implemented various mode-analysis methods to study the energy transfer during the chemical reaction. The energy accumulation to the reactive modes was found to occur in a step-wise and solvent-dependent manner.

Reaction dynamics of Claisen rearrangement simulated in different solvent

256th National Meeting (2018)

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