Kinetic activation-relaxation technique: An off-lattice self-learning kinetic Monte Carlo algorithm

Normand Mousseau
University of Montreal
Département de physique

Most successful accelerated methods of all, Kinetic Monte Carlo (KMC) schemes have
been used for a few decades to simulate activated processes on experimental time
scales. 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
present a flexible approach — the kinetic activation-relaxation technique (kinetic
ART) — which lifts these constraints. Our method is based on an off-lattice,
self-learning, on-the-?y identification and evaluation of activation barriers using
ART nouveau and a topological description of events. It allows us to fully take into account long-range elastic deformation and treat configurations that could not have been foreseen at the onset. Workings of the method will be demonstrated on defects in Si.

Fedwa El-Mellouhi, Normand Mousseau and Laurent J. Lewis, Département de physique and Regroupement québécois sur les matériaux de pointe, Université de Montréal, Montréal, Québec, Canada

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