Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders

Published

May 25, 2022

Author(s)

Jacob Monroe, Vincent K. Shen

Abstract

Discovering meaningful collective variables for enhancing sampling, via applied biasing potentials or tailored MC move sets, remains a major challenge within molecular simulation. While recent studies identifying collective variables with variational autoencoders (VAEs) have focused on the encoding and latent space discovered by a VAE, the impact of the decoding and its ability to act as a generative model remains unexplored. We demonstrate how VAEs may be used to learn (on-the-fly and with minimal human intervention) highly efficient, collective Monte Carlo moves that accelerate sampling along the learned collective variable. In contrast to many machine learning-based efforts to bias sampling and generate novel configurations, our methods result in exact sampling in the ensemble of interest and do not require reweighting. In fact, we show that the acceptance rates of our moves approach unity for a perfect VAE model. While this is never observed in practice, VAE-based Monte Carlo moves still enhance sampling of new configurations. We demonstrate, however, that the form of the encoding and decoding distributions, in particular the extent to which the decoder reflects the underlying physics, greatly impacts the performance of the trained VAE.

Citation

Journal of Chemical Theory and Computation

Pub Type

Journals

Download Paper

https://doi.org/10.1021/acs.jctc.2c00110

Local Download

Keywords

Molecular simulation, variational autoencoders, VAEs, coarse graining

Citation

Monroe, J. and Shen, V. (2022), Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders, Journal of Chemical Theory and Computation, [online], https://doi.org/10.1021/acs.jctc.2c00110, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932532 (Accessed October 8, 2025)