Transition Path Sampling with Boltzmann Generator-based MCMC Moves

• URL: http://arxiv.org/abs/2312.05340v2
• Date: Tue, 28 May 2024 14:50:41 GMT
• Title: Transition Path Sampling with Boltzmann Generator-based MCMC Moves
• Authors: Michael Plainer, Hannes Stärk, Charlotte Bunne, Stephan Günnemann,
• Abstract summary: 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.
• Score: 49.69940954060636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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