Efficient sampling of equilibrium states

Abstract

Machine Learning Molecular dynamics or Monte Carlo methods can be used to sample equilibrium states, but these methods become computationally expensive for complex systems, where the transition from one equilibrium state to another may only occur through rare events. Noe et al. used neural networks and deep learning to generate distributions of independent soft condensed-matter samples at equilibrium (see the Perspective by Tuckerman). Supervised training is used to construct invertible transformations between the coordinates of the complex system of interest and simple Gaussian coordinates of the same dimensionality. Thus, configurations can be sampled in this simpler coordinate system and then transformed back into the complex one using the correct statistical weighting. Science , this issue p. [eaaw1147][1]; see also p. [982][2] [1]: /lookup/doi/10.1126/science.aaw1147 [2]: /lookup/doi/10.1126/science.aay2568