Migration barriers for surface diffusion on a rigid lattice: challenges and solutions

Abstract

Atomistic rigid lattice Kinetic Monte Carlo is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious part of constructing any Kinetic Monte Carlo model is, however, to calculate all migration barriers that are needed to give the probabilities for any atom jump event to occur in the simulations. One of the common methods of barrier calculations is Nudged Elastic Band. The number of barriers needed to fully describe simulated systems is typically between hundreds of thousands and millions. Calculations of such a large number of barriers of various processes is far from trivial. In this paper, we will discuss the challenges arising during barriers calculations on a surface and present a systematic and reliable tethering force approach to construct a rigid lattice barrier parameterization of face-centred and body-centred cubic metal lattices. We have produced several different barrier sets for Cu and for Fe that can be used for KMC simulations of processes on arbitrarily rough surfaces. The sets are published as Data in Brief articles and available for the use.

References in corpus (7)

• The Kinetic Activation-Relaxation Technique: A Powerful Off-lattice On-the-fly Kinetic Monte Carlo Algorithm
• Self-learning Kinetic Monte-Carlo method: application to Cu(111)
• Updated baseline for a staged Compact Linear Collider
• Simulation of the nanostructure evolution under irradiation in Fe-C alloys
• Genetic Programming for Multi-Timescale Modeling
• Electromigration occurrences and its effects on metallic surfaces submitted to high electromagnetic field : A novel approach to breakdown in accelerators
• Long-term stability of Cu surface nanotips

Cited by in corpus (9)

• Au nanowire junction breakup through surface atom diffusion
• Growth mechanism for nanotips in high electric fields
• Biased self-diffusion on Cu surface due to electric field gradients
• DC vacuum breakdown in an external magnetic field
• Cu self-sputtering MD simulations for 0.1-5 keV ions at elevated temperatures
• Tungsten migration energy barriers for surface diffusion: a parameterization for KMC simulations
• Data sets and trained neural networks for Cu migration barriers
• Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics
• Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion