  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.
