@inproceedings{
plainer2023transition,
title={Transition Path Sampling with Boltzmann Generator-based {MCMC} Moves},
author={Michael Plainer and Hannes Stark and Charlotte Bunne and Stephan G{\"u}nnemann},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=KWwwQ97HfS}
}
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.
Transition Path Sampling with Boltzmann Generator-based MCMC Moves
Michael Plainer Hannes Stark Charlotte Bunne Stephan Günnemann