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Kinetic monte carlo (KMC) concepts
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* 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