Enhanced Sampling Methods for Molecular Dynamics

Enhanced Sampling Methods for Molecular Dynamics: Algorithms, Implementations, and Applications provides a single introductory resource for understanding enhanced sampling techniques for molecular dynamics studies of equilibrium and kinetics, discussing the theory, algorithm, and implementation of techniques for equilibrium studies, such as Umbrella Sampling, Replica Exchange, Generalized Ensembles, and Metadynamics. A similar discussion of methodologies for enhanced sampling for kinetics then follows. Ron Elber considers exact and approximate approaches of enhanced sampling, their speed, rate of convergence, and accuracy. He examines the necessary inputs of these approaches, such as prior knowledge of the reaction coordinate (or several coarse variables). The chapters consider path integral formulation, Weighted Ensemble, Transition Path Sampling, and Milestoning. Finally, simple, detailed examples illustrate the enhancements and prepare the reader for their use in more complex systems. Enhanced Sampling Methods for Molecular Dynamics: Algorithms, Implementations, and Applications is written primarily for computational chemists and biochemists (graduate students and postdoctoral fellows) as well as computational and theoretical scientists who study molecular processes. Experimentalists in the biophysics and biochemistry fields, as well as practitioners in the drug and material design areas who use standard software tools to conduct MD simulations of their experimental systems will also find the book of interest.

• Outlines the rigorous formulation and comparison of different algorithms and provides simple, practical “toy” models to practice and learn how to use them for MD
• Includes analysis of “real life” complex applications to better appreciate the capabilities of enhanced sampling approaches
• Helps the reader answer critical questions in their own work: what are the bottlenecks involved in simulating a system, what enhanced simulation methods would fit my specific system, what observables are computable, and how to do I analyse the results effectively?