An off-lattice, self-learning kinetic Monte Carlo method using local environments.
Journal of Chemical Physics
American Institute of Physics
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Konwar, D., Bhute, V., & Chatterjee, A. (2011). An off-lattice, self-learning kinetic Monte Carlo method using local environments.. Journal of Chemical Physics, 135 (17), 174103-174103. https://doi.org/10.1063/1.3657834
We present a method called local environment kinetic Monte Carlo (LE-KMC) method for efficiently performing off-lattice, self-learning kinetic Monte Carlo (KMC) simulations of activated processes in material systems. Like other off-lattice KMC schemes, new atomic processes can be found on-the-fly in LE-KMC. However, a unique feature of LE-KMC is that as long as the assumption that all processes and rates depend only on the local environment is satisfied, LE-KMC provides a general algorithm for (i) unambiguously describing a process in terms of its local atomic environments, (ii) storing new processes and environments in a catalog for later use with standard KMC, and (iii) updating the system based on the local information once a process has been selected for a KMC move. Search, classification, storage and retrieval steps needed while employing local environments and processes in the LE-KMC method are discussed. The advantages and computational cost of LE-KMC are discussed. We assess the performance of the LE-KMC algorithm by considering test systems involving diffusion in a submonolayer Ag and Ag-Cu alloy films on Ag(001) surface.
This work was supported by Indian Institute of Technology Kanpur Start-up grant IITK/CHE/20100105
External DOI: https://doi.org/10.1063/1.3657834
This record's URL: https://www.repository.cam.ac.uk/handle/1810/293505