Enhanced Sampling with Machine Learning: A Review
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 156 references that are in the map. First posted on arXiv 15 Jun 2023.
Builds on
30 of 156 references.
References · 30
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18 more
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Citing papers per year: 2023: 1.
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