Transition Path Sampling with Boltzmann Generator-based MCMC Moves

TL;DR

This paper introduces a novel method for sampling transition paths in molecular systems using Boltzmann generator-based MCMC moves in latent space, reducing reliance on time-consuming molecular dynamics simulations.

Contribution

It presents a new approach that operates in the latent space of a normalizing flow to efficiently sample transition paths without molecular simulations.

Findings

Successful application to alanine dipeptide

Exact sampling with Metropolis-Hastings in latent space

Evaluation of different latent proposal mechanisms

Abstract

Sampling all possible transition paths between two 3D states of a molecular system has various applications ranging from catalyst design to drug discovery. Current approaches to sample transition paths use Markov chain Monte Carlo and rely on time-intensive molecular dynamics simulations to find new paths. Our approach operates in the latent space of a normalizing flow that maps from the molecule's Boltzmann distribution to a Gaussian, where we propose new paths without requiring molecular simulations. Using alanine dipeptide, we explore Metropolis-Hastings acceptance criteria in the latent space for exact sampling and investigate different latent proposal mechanisms.