Studies of concentration and temperature dependencies of precipitation kinetics in iron-copper alloys using kinetic monte carlo and stochastic statistical simulations

TL;DR

This study compares kinetic Monte Carlo and stochastic statistical methods to simulate precipitation kinetics in iron-copper alloys, revealing their agreement and extending simulation times to better match experimental data.

Contribution

It introduces an improved stochastic statistical method that, combined with kinetic Monte Carlo, accurately models precipitation kinetics over extended timescales in Fe-Cu alloys.

Findings

SSM agrees well with KMCM in describing precipitation kinetics.

Simulations align with experimental data within statistical errors.

Alloying elements influence precipitation stages in Fe-Cu alloys.

Abstract

The earlier-developed ab initio model and the kinetic Monte Carlo method (KMCM) are used to simulate precipitation in a number of iron-copper alloys with different copper concentrations x and temperatures T. The same simulations are also made using the improved version of the earlier-suggested stochastic statistical method (SSM). The results obtained enable us to make a number of general conclusions about the dependencies of the decomposition kinetics in Fe-Cu alloys on x and T. We also show that the SSM describes the precipitation kinetics in a fair agreement with the KMCM, and employing the SSM in conjunction with the KMCM enables us to extend the KMC simulations to the longer evolution times. The results of simulations seem to agree with available experimental data for Fe-Cu alloys within statistical errors of simulations and the scatter of experimental results. Comparison of results of simulations to experiments for some multicomponent Fe-Cu-based alloys enables us to make certain conclusions about the influence of alloying elements in these alloys on the precipitation kinetics at different stages of evolution.