Enhanced Sampling with Machine Learning: A Review

• URL: http://arxiv.org/abs/2306.09111v2
• Date: Fri, 16 Jun 2023 15:18:23 GMT
• Title: Enhanced Sampling with Machine Learning: A Review
• Authors: Shams Mehdi, Zachary Smith, Lukas Herron, Ziyue Zou and Pratyush Tiwary
• Abstract summary: Molecular dynamics (MD) enables the study of physical sampling systems with excellent resolution but suffers from severe time-scale limitations. To address this, enhanced sampling methods have been developed to improve explorationtemporalal space. In recent years, integration of machine learning (ML) techniques in different domains has shown promise. This review explores the merging of ML and enhanced MD by presenting different shared viewpoints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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