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.

翻译：分子动力学（MD）能够以优异的时空分辨率研究物理系统，但受限于严重的时间尺度限制。为解决这一问题，增强采样方法被开发出来以改善构型空间的探索效率。然而，实现这些方法具有挑战性且需要领域专业知识。近年来，机器学习（ML）技术在不同领域的集成展现出巨大潜力，并推动其在增强采样中的广泛应用。尽管ML因数据驱动特性被广泛应用于各类领域，但其与增强采样的融合因存在众多共有的底层协同机制而更为自然。本综述通过呈现多种共同视角来探讨ML与增强MD的融合，为这一快速发展的领域提供全面概述（该领域的最新进展常令人难以追踪）。我们重点介绍了降维、强化学习和基于流的方法等成功策略，最后讨论了ML-增强MD交叉领域中令人振奋的开放性问题。