Enhanced Sampling for Free Energy Profiles with Post-Transition-State Bifurcations

Area of Science:

• Computational Chemistry
• Chemical Dynamics
• Reaction Mechanism Elucidation

Background:

• Exploring free energy landscapes of chemical reactions is crucial for understanding reaction mechanisms.
• Post-transition-state bifurcations present significant challenges in accurately mapping reaction pathways.
• Traditional methods like ab initio molecular dynamics are computationally intensive.

Purpose of the Study:

• To develop an efficient enhanced sampling strategy for exploring free energy landscapes with post-transition-state bifurcations.
• To obtain accurate energetics without relying on expensive quantum chemical calculations.
• To visualize and characterize complex reaction pathways, including bifurcations.

Main Methods:

• Utilized well-tempered metadynamics for enhanced sampling.
• Employed a free energy perturbation scheme combined with a deep learning estimator for accurate single-point energy calculations.
• Defined interpretable collective variables to construct quantitative free energy surfaces.

Main Results:

• Successfully mapped free energy landscapes for reactions with post-transition-state bifurcations.
• Demonstrated the method's accuracy using the SpnF-catalyzed Diels-Alder reaction, identifying both [4+2] and [6+4] cycloadduct pathways.
• Characterized the mechanistic continuum between reaction channels without needing to locate transition state structures.

Conclusions:

• The proposed strategy offers an efficient and accurate approach to study complex chemical reactions.
• The method provides clear visualization of bifurcations, aiding in mechanistic interpretation.
• This technique facilitates the understanding of stereochemical pathways and reaction channel continuum.