Everything everywhere all at once: a probability-based enhanced sampling approach to rare events

TL;DR

This paper introduces a novel probability-based enhanced sampling method combining committor function computation with metadynamics-like techniques, improving the study of rare events and transition states in complex systems.

Contribution

It presents an integrated approach that enhances rare event sampling by combining committor-based variational methods with a logarithmic collective variable, enabling detailed analysis of transition states.

Findings

Accurate sampling of free energy surfaces achieved.

Effective in systems with multiple reactive paths.

Provides physical insights from sampled data.

Abstract

The problem of studying rare events is central to many areas of computer simulations. In a recent paper [Kang, P., et al., Nat. Comput. Sci. 4, 451-460, 2024], we have shown that a powerful way of solving this problem passes through the computation of the committor function, and we have demonstrated how the committor can be iteratively computed in a variational way and the transition state ensemble efficiently sampled. Here, we greatly ameliorate this procedure by combining it with a metadynamics-like enhanced sampling approach in which a logarithmic function of the committor is used as a collective variable. This integrated procedure leads to an accurate and balanced sampling of the free energy surface in which transition states and metastable basins are studied with the same thoroughness. We also show that our approach can be used in cases in which competing reactive paths are possible and intermediate metastable are encountered. In addition, we demonstrate how physical insights can be obtained from the optimized committor model and the sampled data, thus providing a full characterization of the rare event under study. We ascribe the success of this approach to the use of a probability-based description of rare events.