Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling

We present a new machine learning method called multiscale reweighted stochastic embedding (MRSE) [1] for automatically constructing collective variables (CVs) to represent and drive the sampling of free energy landscapes in enhanced sampling simulations. The technique automatically finds CVs by learning a low-dimensional embedding of the high-dimensional feature space to the latent space via a deep neural network. Our work builds upon the popular t-distributed stochastic neighbor embedding approach [2]. We introduce several new aspects to stochastic neighbor embedding algorithms that make MRSE especially suitable for enhanced sampling simulations: (1) a well-tempered landmark selection scheme; (2) a multiscale representation of the high-dimensional feature space; and (3) a reweighting procedure to account for biased training data. We show the performance of MRSE by applying it to several model systems.

[1] J. Rydzewski, and O. Valsson, arXiv:2007.06377. https://arxiv.org/abs/2007.06377
[2] L. Maaten, and G. Hinton. J. Mach. Learn. Res. (2008) 9, 2579-2605.