Enhanced Sampling Methods for Molecular Dynamics

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.

• 1. Introduction: “To understand it, simulate it”2. Coarse variables and reaction coordinates3. Rough energy landscapes, why is it a problem?4. Computational statistical mechanics of equilibrium5. Computational and experimental observables in equilibrium7. The first enhanced sampling method is umbrella sampling8. Computing free energy differences9. Flattening free energy landscapes as a function of coarse variables10. The energy as a reaction coordinate11. The temperature as a reaction coordinate12. Sampling kinetic observables with trajectories13. Computing reaction coordinates from reactive trajectories14. Statistical Learning of reaction space15. Enhancing the sampling of complete trajectories16. Exact estimation of the fluxes of reactive trajectories17. The first hitting point distribution18. Approximating the first hitting point distribution19. Computing kinetic observables with trajectory fragments20. Kinetics on a network21. Experimental data as a tool to enhance simulations22. Simulating very large systems23. Which method should I use?24. Discussion of remaining challenges