Gaussian Mixture Based Enhanced Sampling

TL;DR

This paper introduces a novel enhanced sampling method for molecular dynamics that models metastable states using Gaussian mixtures, enabling efficient exploration of rare events and straightforward calculation of transition rates.

Contribution

The method constructs a Gaussian mixture-based model of the probability density to derive bias potentials, improving sampling efficiency and transition rate estimation.

Findings

Effective sampling of metastable states achieved

Transition rates can be computed directly from biased dynamics

Model adapts to different metastable configurations

Abstract

Many processes in chemistry and physics take place on timescales that cannot be explored using standard molecular dynamics simulations. This renders the use of enhanced sampling mandatory. Here we introduce an enhanced sampling method that is based on constructing a model probability density from which a bias potential is derived. The model relies on the fact that in a physical system most of the configurations visited can be grouped into isolated metastable islands. To each island we associate a distribution that is fitted to a Gaussian mixture. The different distributions are linearly combined together with coefficients that are computed self consistently. Remarkably, from this biased dynamics, rates of transition between different metastable states can be straightforwardly computed.