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An off-lattice, self-learning kinetic Monte Carlo method using local environments.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Konwar, Dhrubajit 
Bhute, Vijesh J 
Chatterjee, Abhijit 

Abstract

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.

Description

Keywords

chemistry computing, materials science, materials science computing, Monte Carlo methods, silver, silver alloys

Journal Title

Journal of Chemical Physics

Conference Name

Journal ISSN

1089-7690
1089-7690

Volume Title

135

Publisher

American Institute of Physics
Sponsorship
This work was supported by Indian Institute of Technology Kanpur Start-up grant IITK/CHE/20100105