# Unfolding Hidden Barriers by Active Enhanced Sampling

## TL;DR

This paper introduces an active learning enhanced sampling method that uses deep neural networks to iteratively identify and lift hidden barriers in complex systems, improving sampling efficiency.

## Contribution

It presents a novel active learning scheme combining neural network-based CVs with enhanced sampling to lift degeneracies and explore energy landscapes more effectively.

## Key findings

- Successfully lifts hidden barriers in energy landscapes.
- Improves sampling efficiency and completeness.
- Preserves kinetic characteristics during exploration.

## 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 (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.