• DOI:10.1103/PhysRevB.72.115401
• Corpus ID: 2416262

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

@article{Trushin2005SelflearningKM,
  title={Self-learning kinetic Monte Carlo method: Application to Cu(111)},
  author={Oleg Trushin and Altaf Karim and Abdelkader Kara and Talat S. Rahman},
  journal={Physical Review B},
  year={2005},
  volume={72},
  pages={115401}
}

• O. Trushin, A. Karim, +1 author T. Rahman
• Published 14 July 2005
• Materials Science
• Physical Review B

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 single- or multiatom processes, within a specific interaction range, are either computed accurately using a saddle-point search procedure, or retrieved from a database in which previously encountered processes are stored. This self-learning procedure enhances the speed of the simulations along with a substantial gain in reliability because of the inclusion of many-particle processes. Accompanying results from the application of the method to the case of two-dimensional Cu adatom-cluster diffusion and coalescence on Cu111 with detailed statistics of involved atomistic processes and contributing diffusion coefficients attest to the suitability of the method for the purpose.