A micro-macro Markov chain Monte Carlo method for molecular dynamics using reaction coordinate proposals I: direct reconstruction

TL;DR

This paper presents a novel micro-macro MCMC algorithm that efficiently samples molecular systems with time-scale separation by combining macroscopic proposals with microscopic reconstruction, improving exploration of metastable states.

Contribution

The paper introduces a new mM-MCMC method that enhances sampling efficiency in molecular dynamics with slow reaction coordinates through a two-level accept-reject scheme.

Findings

Algorithm achieves higher sampling efficiency in test cases.

Convergence of the method is analytically proven.

Numerical experiments demonstrate significant computational gains.

Abstract

We introduce a new micro-macro Markov chain Monte Carlo method (mM-MCMC) to sample invariant distributions of molecular dynamics systems that exhibit a time-scale separation between the microscopic (fast) dynamics, and the macroscopic (slow) dynamics of some low-dimensional set of reaction coordinates. The algorithm enhances exploration of the state space in the presence of metastability by allowing larger proposal moves at the macroscopic level, on which a conditional accept-reject procedure is applied. Only when the macroscopic proposal is accepted, the full microscopic state is reconstructed from the newly sampled reaction coordinate value and is subjected to a second accept/reject procedure. The computational gain stems from the fact that most proposals are rejected at the macroscopic level, at low computational cost, while microscopic states, once reconstructed, are almost always accepted. We analytically show convergence and discuss the rate of convergence of the proposed algorithm, and numerically illustrate its efficiency on a number of standard molecular test cases. We also investigate the effect of the choice of different numerical parameters on the efficiency of the resulting mM-MCMC method.