Enhanced Sampling Method in Statistical Physics and Large-Scale Molecular Simulation of Complex Systems
Master of Science
In large-scale complex systems, traditional computational methods in equilibrium statistical mechanics such as Monte Carlo simulation and molecular dynamics in canonical ensemble often face the broken ergodicity issue, which highly reduces the performance and accuracy of simulation. The past decades have witnessed the development of generalized ensemble, which has significantly enhanced the efficiency of molecular simulation. In this thesis, we get a review of typical generalized ensembles, such as multi-canonical ensemble, parallel tempering, simulating tempering and continuous simulated tempering (CST). We also present a method called parallel continuous simulated tempering(PCST) for enhanced sampling in studying large complex. It mainly inherits and CST method in previous work, while adopts the spirit of parallel tempering, by employing multiple copies with different temperature distributions. The sampling efficiency of PCST was tested in two-dimensional Ising model, Lennard-Jones liquid and all-atom folding simulation of a small globular protein trp-cage in explicit solvent. The results demonstrate that the PCST method has significantly improved sampling efficiency compared with other methods and it is particularly effective in simulating systems with long relaxation time or correlation time.
Enhanced sampling; Statistical mechanics; Simulated tempering; Parallel tempering; Protein folding