Self-learning kinetic Monte Carlo simulations of diffusion in ferromagneticα-Fe–Si alloys

Abstract

Diffusion of Si atom and vacancy in the A2-phase of α-Fe-Si alloys in the ferromagnetic state, with and without magnetic order and in various temperature ranges, are studied using AKSOME, an on-lattice self-learning KMC code. Diffusion of the Si atom and the vacancy are studied in the dilute limit and up to 12 at.% Si, respectively, in the temperature range 350-700 K. Local Si neighborhood dependent activation energies for vacancy hops were calculated on-the-fly using a broken-bond model based on pairwise interaction. The migration barrier and prefactor for the Si diffusion in the dilute limit were obtained and found to agree with published data within the limits of uncertainty. Simulations results show that the prefactor and the migration barrier for the Si diffusion are approximately an order of magnitude higher, and a tenth of an electron-volt higher, respectively, in the magnetic disordered state than in the fully ordered state. However, the net result is that magnetic disorder does not have a significant effect on Si diffusivity within the range of parameters studied in this work. Nevertheless, with increasing temperature, the magnetic disorder increases and its effect on the Si diffusivity also increases. In the case of vacancy diffusion, with increasing Si concentration, its diffusion prefactor decreases while the migration barrier more or less remained constant and the effect of magnetic disorder increases with Si concentration. Important vacancy-Si/Fe atom exchange processes and their activation barriers were identified, and the effect of energetics on ordered phase formation in Fe-Si alloys are discussed.

An Ab Initio Study of Vacancies in Disordered Magnetic Systems: A Case Study of Fe-Rich Fe-Al Phases

“…In contrast to that, quantum-mechanical calculations can provide atomic-scale site-specific information about each particular type of vacancies including those in magnetic systems, see, e.g., Refs. [1,2,3,4,5,6,7,10,11,12,13,14,15,16,17,18,19,20,21].…”