Deep learning collective variables from transition path ensemble

The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow modes of the system, which are referred to as collective variables. Recently, machine learning methods have been used to learn the collective variables as functions of a large number of physical descriptors. Among many such methods, Deep Targeted Discriminant Analysis has proven to be useful. This collective variable is built from data harvested from short unbiased simulations in the metastable basins. Here, we enrich the set of data on which the Deep Targeted Discriminant Analysis collective variable is built by adding data from the transition path ensemble. These are collected from a number of reactive trajectories obtained using the On-the-fly Probability Enhanced Sampling flooding method. The collective variables thus trained lead to more accurate sampling and faster convergence. The performance of these new collective variables is tested on a number of representative examples.

Automated summary

The study focuses on rare transitions between long lived metastable states in molecular dynamics simulations. Various methods have been proposed to address this challenge, with a recent approach using machine learning techniques to learn collective variables. Specifically, the Deep Targeted Discriminant Analysis method has proven to be useful in constructing collective variables from data collected from short unbiased simulations. The authors enhance this approach by including data from transition path ensembles, resulting in improved sampling and convergence rates.