Transition Path Sampling with Boltzmann Generator-based MCMC Moves

Michael Plainer, Hannes Stark, Charlotte Bunne, Stephan Günnemann

Keywords: Transition Path Sampling, Boltzmann Generator, Normalizing Flow, MCMC

TL;DR: Simulation-free MCMC proposals in the latent space of a Boltzmann Generator to sample transition paths

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.

Submission Number: 79