Self-learning kinetic Monte Carlo method: Application to Cu(111)

Self-learning kinetic Monte Carlo method: Application to Cu(111)

We present a method of performing kinetic Monte Carlo simulations that does not require an a priori list of diffusion processes and their associated energetics and reaction rates. Rather, at any time during the simulation, energetics for all possible singleor multiatom processes, within a specific interaction range, are either computed accurately using a saddle-point search procedure, or retrie...

Self-learning Monte Carlo method

Introduction to the Kinetic Monte Carlo Method

Monte Carlo refers to a broad class of algorithms that solve problems through the use of random numbers. They first emerged in the late 1940’s and 1950’s as electronic computers came into use [1], and the name means just what it sounds like, whimsically referring to the random nature of the gambling at Monte Carlo, Monaco. The most famous of the Monte Carlo methods is the Metropolis algorithm [...

A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111).

The reliability of kinetic Monte Carlo (KMC) simulations depends on accurate transition rates. The self-learning KMC method (Trushin et al 2005 Phys. Rev. B 72 115401) combines the accuracy of rates calculated from a realistic potential with the efficiency of a rate catalog, using a pattern recognition scheme. This work expands the original two-dimensional method to three dimensions. The concom...

First-passage kinetic Monte Carlo method.

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 ...