Enhanced Sampling with Machine Learning

Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe timescale limitations. To address this, enhanced sampling methods have been developed to improve the exploration of configurational space. However, implementing these methods is challenging and requires domain expertise. In recent years, integration of machine learning (ML) techniques into 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…

• AP
  Alfred P. Sloan Foundation
• MA
  Maryland Advanced Research Computing Center
• NI
  National Institutes of Health
• NC
  National Cancer Institute
• NI
  National Institute of General Medical Sciences

  Award: R35GM142719
• DO
  Division of Chemistry