Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders.

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 intere

SUBMITTER: Monroe JI 

PROVIDER: S-EPMC11210279 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature