  Molecular dynamics (MD) enables the study of physical systems with excellent
spatiotemporal resolution but suffers from severe time-scale limitations. To
address this, enhanced sampling methods have been developed to improve
exploration of configurational space. However, implementing these is
challenging and requires domain expertise. In recent years, integration of
machine learning (ML) techniques in different domains has shown promise,
prompting their adoption in enhanced sampling as well. Although ML is often
employed in various fields primarily due to its data-driven nature, its
integration with enhanced sampling is more natural with many common underlying
synergies. This review explores the merging of ML and enhanced MD by presenting
different shared viewpoints. It offers a comprehensive overview of this rapidly
evolving field, which can be difficult to stay updated on. We highlight
successful strategies like dimensionality reduction, reinforcement learning,
and flow-based methods. Finally, we discuss open problems at the exciting
ML-enhanced MD interface.
