Unfolding Hidden Barriers by Active Enhanced Sampling
This work addresses a specific bottleneck in molecular dynamics simulations for researchers in computational chemistry and physics, offering an incremental improvement over existing enhanced sampling methods.
The paper tackles the problem of hidden barriers in collective variable-based enhanced sampling by introducing an active learning scheme that iteratively lifts degeneracies in microscopic configurations, resulting in globally preserved kinetic characteristics through incremental enhancement of sample completeness and CV quality.
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 (AES) 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.