Coarse Grained Molecular Dynamics with Normalizing Flows

TL;DR

This paper introduces a novel sampling algorithm combining normalizing flows and non-equilibrium dynamics to efficiently explore complex energy landscapes in molecular systems, enabling better sampling of metastable states.

Contribution

The authors develop a new Markov chain sampling method that uses normalizing flows to model collective variables and perform non-local updates, improving exploration of free energy landscapes.

Findings

Successfully tested on Gaussian mixture model

Effectively sampled a polymer system with high energy barriers

Produced thermalized configurations and trained flow models

Abstract

We propose a sampling algorithm relying on a collective variable (CV) of mid-size dimension modelled by a normalizing flow and using non-equilibrium dynamics to propose full configurational moves from the proposition of a refreshed value of the CV made by the flow. The algorithm takes the form of a Markov chain with non-local updates, allowing jumps through energy barriers across metastable states. The flow is trained throughout the algorithm to reproduce the free energy landscape of the CV. The output of the algorithm are a sample of thermalized configurations and the trained network that can be used to efficiently produce more configurations. We show the functioning of the algorithm first on a test case with a mixture of Gaussians. Then we successfully test it on a higher dimensional system consisting in a polymer in solution with a compact and an extended stable state separated by a high free energy barrier.