2008/05/15 by Fedwa El‐Mellouhi, fedwa El-Mellouhi, Normand Mousseau +1 · 1 citation
Engineering · Materials Science · Physics and Astronomy · #Ion-surface interactions and analysis #Machine Learning in Materials Science #Semiconductor materials and interfaces #cond-mat.mtrl-sci
paper · pdf · doi:10.1103/physrevb.78.153202
5 pages, 4 figures
arxiv created 2008/05/15 · openalex publication_date 2008/10/07 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Many materials science phenomena are dominated by activated diffusion processes and occur on time scales 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 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 propose a 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. Using this method, we demonstrate that elastic deformations are determinant to the diffusion kinetics of vacancies in Si and are responsible for their trapping.