LAUSR

Abstract Selected solute atoms can strongly interact with and slow down the diffusion of point defects in alloy systems. While such additions can be beneficial, for instance to promote microstructural stability during thermal annealing or during irradiation by energetic particles, they create significant computational challenges when simulating these evolutions using atomistic techniques such as kinetic Monte Carlo (KMC) simulations. Point defect trapping in energy basins created by clusters of solute atoms leads to frequent re-visiting of states with short residence times, which dramatically reduces the efficiency of traditional KMC algorithms. We introduce here a hybrid algorithm that combines and expand on two prior KMC algorithms, the Chain KMC and the equilibrating basin KMC. This hybrid algorithm, referred to as the Equilibrating Chain algorithm, utilizes Chain KMC as previously reported, but leverages the data-handling framework to build an occupation distribution of the basin, allowing the equilibrating basin assumption to be statistically tested and applied. For a model A-B trapping alloy system on a face-centered cubic lattice, statistical comparisons of basin exit and cluster dissolution kinetics between traditional and accelerated KMC algorithms are presented to demonstrate the accuracy and the efficiency of the new algorithm. We also discuss our algorithm in the context of other accelerated KMC algorithms.