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Efficient combination of Wang–Landau and transition matrix Monte Carlo methods for protein simulations
Author(s) -
Ghulghazaryan Ruben G.,
Hayryan Shura,
Hu ChinKun
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20597
Subject(s) - monte carlo method , ergodicity , statistical physics , convergence (economics) , stochastic matrix , interval (graph theory) , matrix (chemical analysis) , hybrid monte carlo , monte carlo molecular modeling , density matrix , algorithm , mathematics , computer science , physics , markov chain monte carlo , quantum mechanics , chemistry , statistics , markov chain , chromatography , combinatorics , economics , quantum , economic growth
An efficient combination of the Wang‐Landau and transition matrix Monte Carlo methods for protein and peptide simulations is described. At the initial stage of simulation the algorithm behaves like the Wang‐Landau algorithm, allowing to sample the entire interval of energies, and at the later stages, it behaves like transition matrix Monte Carlo method and has significantly lower statistical errors. This combination allows to achieve fast convergence to the correct values of density of states. We propose that the violation of TTT identities may serve as a qualitative criterion to check the convergence of density of states. The simulation process can be parallelized by cutting the entire interval of simulation into subintervals. The violation of ergodicity in this case is discussed. We test the algorithm on a set of peptides of different lengths and observe good statistical convergent properties for the density of states. We believe that the method is of general nature and can be used for simulations of other systems with either discrete or continuous energy spectrum. © 2006 Wiley Periodicals, Inc. J Comput Chem 28: 715–726, 2007