Rich Dynamics Underlying Solution Reactions Revealed by Sampling and Data Mining of Reactive Trajectories

Area of Science:

• Chemical Dynamics
• Computational Chemistry
• Physical Chemistry

Background:

• Chemical reactions in solution are influenced by complex solvent-solute interactions.
• Understanding reaction mechanisms requires detailed analysis of thermodynamics, kinetics, and dynamics.

Purpose of the Study:

• To systematically investigate the thermodynamics, kinetics, and molecular dynamics of chemical reactions in solution.
• To optimize reaction coordinates for a (retro-)Claisen rearrangement in water using a Bayesian learning algorithm.

Main Methods:

• Efficient sampling in configuration and trajectory spaces.
• Data mining for mechanism analysis.
• Bayesian learning algorithm for variational optimization of reaction coordinates.

Main Results:

• Identified coupling between bond formation/breakage, intramolecular charge separation, and dipole change.
• Observed significant dynamic solvent effects accelerating the Claisen rearrangement in water.
• Found coupling between vibrational modes, solvation states, and reaction dynamics, leading to heterogeneous and oscillatory reaction paths.
• Interpreted reaction rates using Kramers' theory with a diffusion term for solvent-solute interactions.

Conclusions:

• Solvent-solute interactions profoundly influence reaction dynamics and energy transfer in homogeneous solutions.
• Reaction mechanisms in solution can be highly complex due to dynamic solvent effects.
• Computational methods provide detailed insights into reaction pathways and solvent influences.