Kinetic monte carlo (KMC) conceptsΒΆ

  • Method for simulating dynamic evolution of kinetic processes
  • Stochastically evaluates and evolves ensembles of processes/events
    • Events represent the collective action of many sub-processes occuring at much smaller time and length scales
      • E.G., grain growth on time and length scales well in excess of those involved in the transport of atoms across a grain boundary
  • Processes modeled must have a known rate
    • Rates are probability or number of events per unit time
    • Rates are an input to the KMC algorithm
  • With respect to grain growth in spparks, rates are defined based upon energy differences between initial and finial states of events