Kinetic Activation Relaxation Technique

Abstract

We present a detailed description of the kinetic Activation-Relaxation Technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe and structural relaxation in a-Si (amorphous silicon).

arXiv comment13 pages, 11 figures. Final version as published, Figs. 6 and 7 exchanged, minor typographical and stylistic corrections

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• Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification
• Atomistic-Object Kinetic Monte Carlo simulations of irradiation damage in tungsten
• Slow relaxation of cascade-induced defects in Fe
• Accelerating off-lattice kinetic Monte Carlo simulations to predict hydrogen vacancy-cluster interactions in $\alpha$-Fe
• Cs diffusion in SiC high-energy grain boundaries
• On the effect of electronic stopping in molecular dynamics simulations of collision cascades in Gallium Arsenide
• Carbon Diffusion in Concentrated Fe-C Glasses
• Distribution of atomic rearrangement vectors in a metallic glass
• Reaction-drift-diffusion models from master equations: application to material defects
• First Principles Validation of Energy Barriers in Ni${}_{75}$Al${}_{25}$
• Kinetic Activation-Relaxation Technique and Self-Evolving Atomistic Kinetic Monte Carlo: Comparison of on-the-fly kinetic Monte Carlo algorithms