Accurate estimation of the density of states from Monte Carlo transition probability data.
This study develops an efficient approach for calculating the density of states from energy transition probability matrices generated from extended sampling Monte Carlo simulations. Direct and iterative variants of the method are shown to achieve high accuracy when applied to the two-dimensional Ising model for which the density of states function can be determined exactly. They are also used to calculate the density of states of lattice protein and Lennard-Jones models which generate more complex nonzero matrix structures. Whereas the protein simulations test the method on a system exhibiting a rugged free energy landscape, the Lennard-Jones calculations highlight implementation details that arise in applications to continuous energy systems. Density of states results for these two systems agree with estimates from multiple histogram reweighting, demonstrating that the new method provides an alternative approach for computing the thermodynamic properties of complex systems.
Fenwick
Michael K
MK
Department of Molecular Medicine, College of Veterinary Medicine, Cornell University, Ithaca, New York 14853-6401, USA. mkf7@cornell.edu
D003198:Computer Simulation; D008390:Markov Chains; D008956:Models, Chemical; D009010:Monte Carlo Method; D011506:Proteins / Q000737:chemistry*
D011506:Proteins