Self-Learning Kinetic Monte Carlo Simulations of Al Diffusion in Mg

TL;DR

This study uses self-learning kinetic Monte Carlo simulations to investigate Al atom diffusion in Mg, revealing diffusion anisotropy and providing detailed atomistic mechanisms, with results aligning with experimental data.

Contribution

It introduces a self-learning KMC method for Al diffusion in Mg, including on-the-fly barrier calculations and a lattice mapping scheme for HCP structures.

Findings

Al diffuses faster in the basal plane than along the c-axis.

Effective activation barriers match experimental values, but prefactors are lower.

All relevant vacancy-atom exchange processes and barriers are identified.

Abstract

Vacancy-mediated diffusion of an Al atom in pure Mg matrix is studied using the atomistic, on-lattice self-learning kinetic Monte Carlo (SLKMC) method. Activation barriers for vacancy-Mg and vacancy-Al atom exchange processes are calculated on-the-fly using the climbing image nudged-elastic band method and binary Mg-Al modified embedded-atom method interatomic potential. Diffusivities of an Al atom obtained from SLKMC simulations show the same behavior as observed in experimental and theoretical studies available in the literature, that is, Al atom diffuses faster within the basal plane than along the c-axis. Although, the effective activation barriers for Al-atom diffusion from SLKMC simulations are close to experimental and theoretical values, the effective prefactors are lower than those obtained from experiments. We present all the possible vacancy-Mg and vacancy-Al atom exchange processes and their activation barriers identified in SLKMC simulations. A simple mapping scheme to map an HCP lattice on to a simple cubic lattice is described, which enables the simulation of HCP lattice using on-lattice framework. We also present the pattern recognition scheme which is used in SLKMC simulations to identify the local Al atom configuration around a vacancy.