SPPARKS Kinetic Monte Carlo Simulator

Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. -- John von Neumann
God does not play dice -- Albert Einstein

This is the home page for the kinetic Monte Carlo code SPPARKS, an acronym for Stochastic Parallel PARticle Kinetic Simulator.

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SPPARKS is a parallel Monte Carlo code for on-lattice and off-lattice models that includes algorithms for kinetic Monte Carlo (KMC), rejection kinetic Monte Carlo (rKMC), and Metropolis Monte Carlo (MMC). It implements several KMC solvers whose serial computational complexity ranges from O(N) to O(NlogN) to O(1) in the number of events N owned by a processor. In a generic sense the solvers catalog a list of "events", each with an associated probability, choose a single event to perform, and advance time by the correct amount. Events may be chosen individually at random, or by sweeping over sites in a more ordered fashion.

Applications that are packaged with SPPARKS, or which a user adds to the code, define sites and events and probabilities, as well as how the execution of an event changes the state of the model.

SPPARKS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.

SPPARKS is distributed as an open source code under the terms of the GPL, or sometimes by request under the terms of the GNU Lesser General Public License (LGPL). The current version can be downloaded here.

SPPARKS was developed at Sandia National Laboratories, a US Department of Energy laboratory. The authors are Steve Plimpton and Aidan Thompson and Alex Slepoy who can be contacted at sjplimp at sandia.gov, athomps at sandia.gov, and alexander.slepoy at nnsa.doe.gov, respectively. Funding for SPPARKS development has come from the DOE and is acknowledged here, along with the contributions of others.

Recent SPPARKS News

SPPARKS Highlight

(see the Pictures & Movies page for more examples of SPPARKS calculations)

This is work by Liz Holm (eaholm at sandia.gov) at Sandia and Todd Hoffmann (todd.hoffmann at asu.edu) and his advisor at Carnegie Mellon, to study the mechanisms and statistics associated with abnormal grain growth. In grain growth, when the system is annealed, grain boundaries will move and rearrange so as to increase the average grain size and decrease the grain boundary area per unit volume. In normal grain growth, this occurs relatively uniformly, but in abnormal grain growth, one grain grows at the expense of the others. Abnormal grain growth can have many causes. Here the effects of static particle dispersions on grain growth are studied. Particles should pin grain boundaries in positions that maximize their contact with the particles, so that grain growth cannot proceed. However, occasionally one grain breaks free and is able to grow at the expense of others, leading to abnormal growth.

The images and movies show typical results for a 300x300x300 lattice, run for a million Monte Carlo steps (sweep over all lattice sites), with 5 volume percent pinning particles, inserted when the average grain radius was 10 sites. In the normal case, the system becomes fully pinned. In the second case, abnormal growth eventually occurs.

Two images and two 1.5 Mb AVI movies