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Systematic Control of Collective Variables Learned from Variational Autoencoders

September 7, 2022

Author(s)

Jacob Monroe, Vincent K. Shen

Variational autoencoders (VAEs) are rapidly gaining popularity within molecular simulation for discovering low- dimensional, or latent, representations, which are critical for both analyzing and accelerating simulations. However, it remains unclear how the

Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders

May 25, 2022

Author(s)

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

Extrapolation And Interpolation Strategies For Efficiently Estimating Structural Observables As a Function Of Temperature And Density

October 8, 2020

Author(s)

Jacob I. Monroe, Harold Wickes Hatch, Nathan NMN Mahynski, M. Scott Shell, Vincent K. Shen

Thermodynamic extrapolation has previously been used to predict arbitrary structural observables in molecular simulations at temperatures (or relative chemical potentials in open- system mixtures) different from those at which the simulation was performed

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