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Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders



Jacob Monroe, Vincent K. Shen


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.
Journal of Chemical Theory and Computation


Molecular simulation, variational autoencoders, VAEs, coarse graining


Monroe, J. and Shen, V. (2022), Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders, Journal of Chemical Theory and Computation, [online],, (Accessed April 23, 2024)
Created May 25, 2022, Updated November 29, 2022