MCAMC

MCAMC: an advanced algorithm for kinetic Monte Carlo simulations from magnetization switching to protein folding We present the Monte Carlo with Absorbing Markov Chains (MCAMC) method for extremely long kinetic Monte Carlo simulations. The MCAMC algorithm does not modify the system dynamics. It is extremely useful for models with discrete state spaces when low-temperature simulations are desired. To illustrate the strengths and limitations of this algorithm we introduce a simple model involving random walkers on an energy landscape. This simple model has some of the characteristics of protein folding and could also be experimentally realizable in domain motion in nanoscale magnets. We find that even the simplest MCAMC algorithm can speed up calculations by many orders of magnitude. More complicated MCAMC simulations can gain further increases in speed by orders of magnitude.


References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Cotter, Simon L.: Constrained approximation of effective generators for multiscale stochastic reaction networks and application to conditioned path sampling (2016)
  2. Cristadoro, G.; Knight, G.; Esposti, M. Degli: Follow the fugitive: an application of the method of images to open systems (2013)
  3. Lahbabi, Salma; Legoll, Frédéric: Effective dynamics for a kinetic Monte-Carlo model with slow and fast time scales (2013)
  4. Hurtado, Pablo I.; Marro, J.; Garrido, P. L.: Demagnetization via nucleation of the nonequilibrium metastable phase in a model of disorder (2008)
  5. Ashton, Douglas J.; Hedges, Lester O.; Garrahan, Juan P.: Fast simulation of facilitated spin models (2005)
  6. Lee, Hwee Kuan; Okabe, Yutaka; Cheng, X.; Jalil, M. B. A.: Solving the master equation for extremely long time scale calculations (2005)
  7. Novotny, M. A.; Wheeler, S. M.: MCAMC: an advanced algorithm for kinetic Monte Carlo simulations from magnetization switching to protein folding (2003)
  8. Novotny, M. A.: Low-temperature long-time simulations of Ising ferromagnets using the Monte Carlo with absorbing Markov chains method (2002)
  9. Wilding, Nigel; Landau, David P.: Monte Carlo methods for bridging the timescale gap (2002)
  10. Rikvold, Per Arne; Kolesik, M.: Analytic approximations for the velocity of field-driven Ising interfaces (2000)
  11. Korniss, G.; Novotny, M. A.; Rikvold, P. A.: Parallelization of a dynamic Monte Carlo algorithm: A partially rejection-free conservative approach (1999)
  12. Cirillo, Emilio N. M.; Lebowitz, Joel L.: Metastability in the two-dimensional Ising model with free boundary conditions (1998)