# 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} }

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…

## 103 Citations

### A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)

- PhysicsJournal of physics. Condensed matter : an Institute of Physics journal
- 2012

This work expands the original two-dimensional kinetic Monte Carlo method to three dimensions, and applies the model to the diffusion of Ag monolayer islands on Ag, and the homoepitaxial growth of Ag on Ag(111) at low temperatures.

### Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion

- Materials ScienceComputational Materials Science
- 2020

### Self-learning kinetic Monte Carlo model for arbitrary surface orientations

- Physics
- 2013

While the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface…

### Off-lattice self-learning kinetic Monte Carlo: application to 2D cluster diffusion on the fcc(111) surface

- Materials ScienceJournal of physics. Condensed matter : an Institute of Physics journal
- 2009

This technique combines the ideas embedded in the SLKMC method with a new pattern-recognition scheme fitted to an off-lattice model in which relative atomic positions are used to characterize and store configurations.

### Kinetic Activation-Relaxation Technique and Self-Evolving Atomistic Kinetic Monte Carlo: Comparison of on-the-fly kinetic Monte Carlo algorithms

- Computer Science
- 2014

### Kinetic Monte Carlo Method: Mathematical Foundations and Applications for Physics of Low-Dimensional Nanostructures

- PhysicsMathematical Models and Computer Simulations
- 2018

The kinetic Monte Carlo (kMC) method is an indispensable method for studying atomic and molecular systems, which makes it possible to solve a wide range of problems associated with atomic diffusion,…

### Kinetic Monte Carlo Method: Mathematical Foundations and Applications for Physics of Low-Dimensional Nanostructures

- PhysicsMathematical Models and Computer Simulations
- 2018

The kinetic Monte Carlo (kMC) method is an indispensable method for studying atomic and molecular systems, which makes it possible to solve a wide range of problems associated with atomic diffusion,…

### Monte Carlo and kinetic Monte Carlo methods

- Physics
- 2009

This article reviews the basic computational techniques for carrying out multi-scale simulations using statistical methods, with the focus on simulations of epitaxial growth. First, the…

### Self-learning kinetic Monte Carlo simulations of self-diffusion of small Ag islands on the Ag(111) surface

- PhysicsJournal of physics. Condensed matter : an Institute of Physics journal
- 2016

Self-diffusion of small two-dimensional Ag islands, containing up to ten atoms, on the Ag(111) surface using self-learning kinetic Monte Carlo (SLKMC) simulations finds that two- to seven-atom islands primarily diffuse via concerted translation processes with small contributions from multi-atom and single-atom processes.

### Localized saddle-point search and application to temperature-accelerated dynamics.

- PhysicsThe Journal of chemical physics
- 2013

This work has obtained N-independent scaling for the computational cost of the saddle-point search as a function of system size N and shows significantly improved performance of TAD with the LSAD method, for the case of Ag/Ag( 100) annealing and Cu/Cu(100) growth, while maintaining a negligibly small error in energy barriers.

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