Enhanced Sampling Methods for Molecular Dynamics
Algorithms, Implementations, and Applications
Will be published approx. on 1. July 2027
Book
Paperback/Softback
560 pages
978-0-443-32822-0 (ISBN)
Description
Enhanced Sampling Methods for Molecular Dynamics: Algorithms, Implementations, and Applications covers 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. The book considers exact and approximate approaches of enhanced sampling, their speed, rate of convergence, and accuracy. Chapters consider path integral formulation, Weighted Ensemble, Transition Path Sampling, and Milestoning.
Finally, simple, detailed examples illustrate enhancements and prepare the reader for their use in more complex systems, making this an ideal resource for computational chemists, biochemists (graduate students and postdoctoral fellows), and computational and theoretical scientists who study molecular processes.
Finally, simple, detailed examples illustrate enhancements and prepare the reader for their use in more complex systems, making this an ideal resource for computational chemists, biochemists (graduate students and postdoctoral fellows), and computational and theoretical scientists who study molecular processes.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
College/higher education
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-0-443-32822-0 (9780443328220)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
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