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瓶颈。为克服这一局限，研究者开发了增强采样方法以提升构型空间的探索效率。然而，这些方法的实施颇具挑战性，且需要领域专业知识。近年来，机器学习（ML）技术在不同领域的整合展现出巨大潜力，促使研究者将其引入增强采样领域。尽管ML在许多领域主要因其数据驱动特性得到广泛应用，但其与增强采样的结合更为自然，二者存在诸多共通的底层协同机制。本综述通过呈现不同的共同视角，探讨ML与增强MD的融合。我们全面梳理了这一快速发展的领域——该领域进展之快常令人难以同步跟进。重点介绍了降维、强化学习及基于流的方法等成功策略，最后探讨了ML-增强MD交叉领域中令人振奋的未解问题。