An energy basin finding algorithm for kinetic Monte Carlo acceleration

作者：Puchala Brian^*; Falk Michael L; Garikipati Krishna

来源：Journal of Chemical Physics, 2010, 132(13): 134104.

DOI：10.1063/1.3369627

• adsorbed layers
• arsenic
• diffusion
• Markov processes
• metal clusters
• Monte Carlo methods
• silicon

We present an energy basin finding algorithm for identifying the states in absorbing Markov chains used for accelerating kinetic Monte Carlo (KMC) simulations out of trapping energy basins. The algorithm saves groups of states corresponding to basic energy basins in which there is (i) a minimum energy saddle point and (ii) in moving away from the minimum the saddle point energies do not decrease between successive moves. When necessary, these groups are merged to help the system escape basins of basins. Energy basins are identified either as the system visits states, or by exploring surrounding states before the system visits them. We review exact and approximate methods for accelerating KMC simulations out of trapping energy basins and implement them within our algorithm. Its flexibility to store varying numbers of states, and ability to merge sets of saved states as the program runs, allows it to efficiently escape complicated trapping energy basins. Through simulations of vacancy-As cluster dissolution in Si, we demonstrate our algorithm can be several orders of magnitude faster than standard KMC simulations.

• 出版日期2010-4-7