From enhanced sampling to reaction profiles

TL;DR

This paper introduces a neural network-based method to identify effective collective variables for enhanced sampling, enabling efficient and clear reaction profile determination in complex chemical processes.

Contribution

The authors propose a novel approach that uses neural networks to derive a minimal set of collective variables, improving sampling efficiency and reaction profile clarity.

Findings

Method successfully discriminates metastable basins

Single collective variable suffices for complex reactions

Enhanced sampling efficiency demonstrated in chemical processes

Abstract

The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect a set of data from the different metastable basins. The data are then projected with the help of a neural network into a low-dimensional manifold in which data from different basins are well discriminated. This is here guaranteed by imposing that the projected data follows a preassigned distribution. The collective variables thus obtained lead to an efficient sampling and often allow reducing the number of collective variables in a multi-basin scenario. We first check the validity of the method in two-state systems. We then move to multi-step chemical processes. In the latter case, at variance with previous approaches, one single collective variable suffices, leading not only to computational efficiency but to a very clear representation of the reaction free energy profile.