Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling.

ABSTRACT: Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional la

SUBMITTER: Rydzewski J 

PROVIDER: S-EPMC8389995 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature