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Title: Self-learning kinetic Monte Carlo simulations of diffusion in ferromagnetic α -Fe–Si alloys

Abstract

Diffusion in α-Fe-Si alloys is studied using AKSOME, an on-lattice self-learning KMC code, in the ferromagnetic state. Si diffusivity in the α-Fe matrix were obtained with and without the magnetic disorder in various temperature ranges. In addition we studied vacancy diffusivity in ferromagnetic α-Fe at various Si concentrations up to 12.5at.% in the temperature range of 350–550 K. The results were compared with available experimental and theoretical values in the literature. Local Si-atom dependent activation energies for vacancy hops were calculated using a broken-model and were stored in a database. The migration barrier and prefactors for Si-diffusivity were found to be in reasonable agreement with available modeling results in the literature. Magnetic disorder has a larger effect on the prefactor than on the migration barrier. Prefactor was approximately an order of magnitude and the migration barrier a tenth of an electron-volt higher with magnetic disorder when compared to a fully ferromagnetic ordered state. In addition, the correlation between various have a larger effect on the Si-diffusivity extracted in various temperature range than the magnetic disorder. In the case of vacancy diffusivity, the migration barrier more or less remained constant while the prefactor decreased with increasing Si concentration in the disorderedmore » or A2-phase of Fe-Si alloy. Important vacancy-Si/Fe atom exchange processes and their activation barriers were also identified and discuss the effect of energetics on the formation of ordered phases in Fe-Si alloys.« less

Authors:
ORCiD logo; ; ; ; ORCiD logo
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1414534
Report Number(s):
PNNL-SA-124267
Journal ID: ISSN 0953-8984
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Physics. Condensed Matter; Journal Volume: 30; Journal Issue: 3
Country of Publication:
United States
Language:
English
Subject:
Kinetic Monte Carlo; Vacancy Diffusion; Silicon Steels; AKSOME; Alloys

Citation Formats

Nandipati, Giridhar, Jiang, Xiujuan, Vemuri, Rama S., Mathaudhu, Suveen, and Rohatgi, Aashish. Self-learning kinetic Monte Carlo simulations of diffusion in ferromagnetic α -Fe–Si alloys. United States: N. p., 2017. Web. doi:10.1088/1361-648X/aa9774.
Nandipati, Giridhar, Jiang, Xiujuan, Vemuri, Rama S., Mathaudhu, Suveen, & Rohatgi, Aashish. Self-learning kinetic Monte Carlo simulations of diffusion in ferromagnetic α -Fe–Si alloys. United States. doi:10.1088/1361-648X/aa9774.
Nandipati, Giridhar, Jiang, Xiujuan, Vemuri, Rama S., Mathaudhu, Suveen, and Rohatgi, Aashish. 2017. "Self-learning kinetic Monte Carlo simulations of diffusion in ferromagnetic α -Fe–Si alloys". United States. doi:10.1088/1361-648X/aa9774.
@article{osti_1414534,
title = {Self-learning kinetic Monte Carlo simulations of diffusion in ferromagnetic α -Fe–Si alloys},
author = {Nandipati, Giridhar and Jiang, Xiujuan and Vemuri, Rama S. and Mathaudhu, Suveen and Rohatgi, Aashish},
abstractNote = {Diffusion in α-Fe-Si alloys is studied using AKSOME, an on-lattice self-learning KMC code, in the ferromagnetic state. Si diffusivity in the α-Fe matrix were obtained with and without the magnetic disorder in various temperature ranges. In addition we studied vacancy diffusivity in ferromagnetic α-Fe at various Si concentrations up to 12.5at.% in the temperature range of 350–550 K. The results were compared with available experimental and theoretical values in the literature. Local Si-atom dependent activation energies for vacancy hops were calculated using a broken-model and were stored in a database. The migration barrier and prefactors for Si-diffusivity were found to be in reasonable agreement with available modeling results in the literature. Magnetic disorder has a larger effect on the prefactor than on the migration barrier. Prefactor was approximately an order of magnitude and the migration barrier a tenth of an electron-volt higher with magnetic disorder when compared to a fully ferromagnetic ordered state. In addition, the correlation between various have a larger effect on the Si-diffusivity extracted in various temperature range than the magnetic disorder. In the case of vacancy diffusivity, the migration barrier more or less remained constant while the prefactor decreased with increasing Si concentration in the disordered or A2-phase of Fe-Si alloy. Important vacancy-Si/Fe atom exchange processes and their activation barriers were also identified and discuss the effect of energetics on the formation of ordered phases in Fe-Si alloys.},
doi = {10.1088/1361-648X/aa9774},
journal = {Journal of Physics. Condensed Matter},
number = 3,
volume = 30,
place = {United States},
year = 2017,
month =
}
  • Atomistic on-lattice self-learning kinetic Monte Carlo (SLKMC) method was used to examine the vacancy-mediated diffusion of an Al atom in pure hcp Mg. Local atomic environment dependent activation barriers for vacancy-atom exchange processes were calculated on-the-fly using climbing image nudged-elastic band method (CI-NEB) and using a Mg-Al binary modified embedded-atom method (MEAM) interatomic potential. Diffusivities of vacancy and Al atom in pure Mg were obtained from SLKMC simulations and are compared with values available in the literature that are obtained from experiments and first-principle calculations. Al Diffusivities obtained from SLKMC simulations are lower, due to larger activation barriers and lowermore » diffusivity prefactors, than those available in the literature but have same order of magnitude. We present all vacancy-Mg and vacancy-Al atom exchange processes and their activation barriers that were identified in SLKMC simulations. We will describe a simple mapping scheme to map a hcp lattice on to a simple cubic lattice that would enable hcp lattices to be simulated in an on-lattice KMC framework. We also present the pattern recognition scheme used in SLKMC simulations.« less
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  • Here, this report presents an accelerated kinetic Monte Carlo (KMC) method to compute the diffusivity of hydrogen in hcp metals and alloys, considering both thermally activated hopping and quantum tunneling. The acceleration is achieved by replacing regular KMC jumps in trapping energy basins formed by neighboring tetrahedral interstitial sites, with analytical solutions for basin exiting time and probability. Parameterized by density functional theory (DFT) calculations, the accelerated KMC method is shown to be capable of efficiently calculating hydrogen diffusivity in α-Zr and Zircaloy, without altering the kinetics of long-range diffusion. Above room temperature, hydrogen diffusion in α-Zr and Zircaloy ismore » dominated by thermal hopping, with negligible contribution from quantum tunneling. The diffusivity predicted by this DFT + KMC approach agrees well with that from previous independent experiments and theories, without using any data fitting. The diffusivity along < c > is found to be slightly higher than that along < a >, with the anisotropy saturated at about 1.20 at high temperatures, resolving contradictory results in previous experiments. Demonstrated using hydrogen diffusion in α-Zr, the same method can be extended for on-lattice diffusion in hcp metals, or systems with similar trapping basins.« less