title: First-passage kinetic Monte Carlo method.
abstract: We present an efficient method for Monte Carlo simulations of diffusion-reaction processes. Introduced by us in a previous paper [Phys. Rev. Lett. 97, 230602 (2006)], our algorithm skips the traditional small diffusion hops and propagates the diffusing particles over long distances through a sequence of superhops, one particle at a time. By partitioning the simulation space into nonoverlapping protecting domains each containing only one or two particles, the algorithm factorizes the N -body problem of collisions among multiple Brownian particles into a set of much simpler single-body and two-body problems. Efficient propagation of particles inside their protective domains is enabled through the use of time-dependent Green's functions (propagators) obtained as solutions for the first-passage statistics of random walks. The resulting Monte Carlo algorithm is event-driven and asynchronous; each Brownian particle propagates inside its own protective domain and on its own time clock. The algorithm reproduces the statistics of the underlying Monte Carlo model exactly. Extensive numerical examples demonstrate that for an important class of diffusion-reaction models the algorithm is efficient at low particle densities, where other existing algorithms slow down severely.
authors.affiliation: Lawrence Livermore National Laboratory, Livermore, California 94551, USA.
authors.forename: Tomas
authors.initials: T
authors.lastname: Oppelstrup
authors.forename: Vasily V
authors.initials: VV
authors.lastname: Bulatov
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Donev
authors.forename: Malvin H
authors.initials: MH
authors.lastname: Kalos
authors.forename: George H
authors.initials: GH
authors.lastname: Gilmer
authors.forename: Babak
authors.initials: B
authors.lastname: Sadigh
mesh_terms: D000465:Algorithms; D001703:Biophysics / Q000379:methods*; D004058:Diffusion; D007700:Kinetics; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D011336:Probability; D015203:Reproducibility of Results