The Kinetic Activation-Relaxation Technique: A Powerful Off-lattice On-the-fly Kinetic Monte Carlo Algorithm

TL;DR

The paper introduces the kinetic activation-relaxation technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo method that dynamically identifies activation barriers, enabling simulation of complex materials phenomena on experimental timescales.

Contribution

It presents a novel off-lattice, on-the-fly KMC algorithm that overcomes limitations of fixed barrier sets by dynamically learning activation barriers during simulations.

Findings

Successfully simulated vacancy diffusion in crystalline silicon.

Demonstrated the method's flexibility and accuracy.

Extended the range of problems accessible to KMC simulations.

Abstract

Many materials science phenomena, such as growth and self-organisation, are dominated by activated diffusion processes and occur on timescales that are well beyond the reach of standard-molecular dynamics simulations. Kinetic Monte Carlo (KMC) schemes make it possible to overcome this limitation and achieve experimental timescales. However, most KMC approaches proceed by discretizing the problem in space in order to identify, from the outset, a fixed set of barriers that are used throughout the simulations, limiting the range of problems that can be addressed. Here, we propose a more flexible approach -- the kinetic activation-relaxation technique (k-ART) -- which lifts these constraints. Our method is based on an off-lattice, self-learning, on-the-fly identification and evaluation of activation barriers using ART and a topological description of events. The validity and power of the method are demonstrated through the study of vacancy diffusion in crystalline silicon.