Enhanced Sampling Methods for Molecular Dynamics
Algorithms, Implementations, and Applications
- 1st Edition - May 1, 2026
- Latest edition
- Author: Ron Elber
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 2 8 2 2 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 2 8 2 3 - 7
Enhanced Sampling Methods for Molecular Dynamics: Algorithms, Implementations, and Applications provides a single introductory resource for understanding enhanced sampling techni… Read more
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?
Computational chemists and biochemists are the most relevant target audience; Computational and theoretical scientists (graduate students and postdoctoral fellows) who study molecular processes will also benefit
1. Introduction: “To understand it, simulate it”
2. Coarse variables and reaction coordinates
3. Rough energy landscapes, why is it a problem?
4. Computational statistical mechanics of equilibrium
5. Computational and experimental observables in equilibrium
7. The first enhanced sampling method is umbrella sampling
8. Computing free energy differences
9. Flattening free energy landscapes as a function of coarse variables
10. The energy as a reaction coordinate
11. The temperature as a reaction coordinate
12. Sampling kinetic observables with trajectories
13. Computing reaction coordinates from reactive trajectories
14. Statistical Learning of reaction space
15. Enhancing the sampling of complete trajectories
16. Exact estimation of the fluxes of reactive trajectories
17. The first hitting point distribution
18. Approximating the first hitting point distribution
19. Computing kinetic observables with trajectory fragments
20. Kinetics on a network
21. Experimental data as a tool to enhance simulations
22. Simulating very large systems
23. Which method should I use?
24. Discussion of remaining challenges
2. Coarse variables and reaction coordinates
3. Rough energy landscapes, why is it a problem?
4. Computational statistical mechanics of equilibrium
5. Computational and experimental observables in equilibrium
7. The first enhanced sampling method is umbrella sampling
8. Computing free energy differences
9. Flattening free energy landscapes as a function of coarse variables
10. The energy as a reaction coordinate
11. The temperature as a reaction coordinate
12. Sampling kinetic observables with trajectories
13. Computing reaction coordinates from reactive trajectories
14. Statistical Learning of reaction space
15. Enhancing the sampling of complete trajectories
16. Exact estimation of the fluxes of reactive trajectories
17. The first hitting point distribution
18. Approximating the first hitting point distribution
19. Computing kinetic observables with trajectory fragments
20. Kinetics on a network
21. Experimental data as a tool to enhance simulations
22. Simulating very large systems
23. Which method should I use?
24. Discussion of remaining challenges
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
RE
Ron Elber
Ron Elber studied chemistry and physics at the Hebrew University of Jerusalem and received his BSc degree in 1981. He continued his studies toward a Ph.D. at the Hebrew University in theoretical chemistry, which he obtained in 1984. He was on the faculty of the University of Illinois at Chicago, the Hebrew University, Cornell University, and the University of Texas at Austin. At present, He is retired from the University of Texas at Austin but is still a core faculty at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin and a Founder of the company MiTOMED Pharma. For almost give decades he has worked in the field of computational statistical mechanics and Molecular Dynamics simulations of biological systems. He introduced several new methodologies that include techniques to compute reaction pathways in complex systems and the method of Milestoning to extend the time scales of straightforward Molecular Dynamics simulation. He has more than 220 publications and an H index of 63.
Affiliations and expertise
Director and Professor (Retired), Center for Computational Life Sciences and Biology, Oden Institute for Computational Engineering & Sciences, The University of Texas at Austin, USA