Unfolding Hidden Barriers by Active Enhanced Sampling

Physical Review Letters
Jing Zhang, Ming Chen

Abstract

Collective variable (CV) or order parameter based enhanced sampling algorithms have achieved great success due to their ability to efficiently explore the rough potential energy landscapes of complex systems. However, the degeneracy of microscopic configurations, originating from the orthogonal space perpendicular to the CVs, is likely to shadow "hidden barriers" and greatly reduce the efficiency of CV-based sampling. Here we demonstrate that systematic machine learning CV, through enhanced sampling, can iteratively lift such degeneracies on the fly. We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler. Our active enhanced sampling algorithm is capable of identifying the least informative regions based on a historical sample, forming a positive feedback loop between the CV learner and sampler. This approach is able to globally preserve kinetic characteristics by incrementally enhancing both sample completeness and CV quality.

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Citations

Sep 7, 2019·Science·Mark E Tuckerman
Jun 17, 2019·The Journal of Chemical Physics·Jakub Rydzewski, Omar Valsson
Jan 11, 2020·Physical Review Letters·Jutta RogalMark E Tuckerman
Jan 19, 2020·Biophysical Journal·Oliver FleetwoodLucie Delemotte
Mar 5, 2021·Journal of Chemical Theory and Computation·Ferry HooftBernd Ensing
Jul 3, 2021·The Journal of Physical Chemistry. a·Jakub Rydzewski, Omar Valsson
Oct 6, 2018·The Journal of Physical Chemistry Letters·Motoyuki Shiga, Mark E Tuckerman
Jan 7, 2022·Journal of the American Chemical Society·Susanta HaldarShozeb Haider

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