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

TL;DR

This paper introduces an efficient enhanced sampling method using well-tempered metadynamics combined with deep learning and free energy perturbation to accurately explore free energy landscapes of reactions with post-transition-state bifurcations, exemplified by a Diels-Alder reaction.

Contribution

The authors develop a computationally efficient approach that captures bifurcations in free energy landscapes without explicit transition state searches, integrating deep learning and perturbation techniques.

Findings

Accurately maps free energy surfaces with bifurcations.

Demonstrates method on a complex cycloaddition reaction.

Provides mechanistic insights into reaction pathways.

Abstract

We present a method to explore the free energy landscapes of chemical reactions with post-transition-state bifurcations using an enhanced sampling method based on well-tempered metadynamics. Obviating the need for computationally expensive DFT-level ab initio molecular dynamics simulations, we obtain accurate energetics by utilizing a free energy perturbation scheme and deep learning estimator for the single-point energies of substrate configurations. Using a pair of easily interpretable collective variables, we present a quantitative free energy surface that is compatible with harmonic transition state theory calculations and in which the bifurcations are clearly visible. We demonstrate our approach with the example of the SpnF-catalyzed Diels-Alder reaction, a cycloaddition reaction in which post-transition-state bifurcation leads to the [4+2] as well as the [6+4] cycloadduct. We obtain the free energy landscapes for different stereochemical reaction pathways and characterize the mechanistic continuum between relevant reaction channels without explicitly searching for the pertinent transition state structures.