Multiscale Modeling of Vacancy-Cluster Interactions and Solute Clustering Kinetics in Multicomponent Alloys

TL;DR

This paper presents an integrated multiscale computational framework combining kinetic Monte Carlo, Markov chain, and cluster dynamics models to predict vacancy-cluster interactions and solute clustering kinetics in multicomponent alloys, specifically Al-Mg-Zn.

Contribution

It introduces a novel combined modeling approach to quantitatively analyze vacancy and cluster interactions across multiple scales in complex alloys.

Findings

Quench rate significantly affects natural aging kinetics.

Two-stage behavior of vacancy-cluster binding energy identified.

Framework accurately predicts long-term cluster evolution.

Abstract

Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales. Here, we develop an integrated computational framework combining on-lattice kinetic Monte Carlo (KMC) simulations, absorbing Markov chain models, and mesoscale cluster dynamics (CD) to investigate these interactions in Al-Mg-Zn alloys. The Markov chain model yields vacancy escape times from solute clusters and identifies a two-stage behavior of the vacancy-cluster binding energy. These binding energies are used to estimate residual vacancy concentrations in the Al matrix after quenching, which serve as critical inputs to CD simulations to predict long-term cluster evolution kinetics during natural aging. Our results quantitatively demonstrate the significant impact of quench rate on natural aging kinetics. Results provide insights to guide alloy chemistry, quench rates, and aging time at finite temperature to control the evolution of solute clusters and eventual precipitates in aged multicomponent alloys.