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An optimized species-conserving Monte Carlo method with potential applicability to high entropy alloys.

Authors :
Fall, Aziz
Grasinger, Matthew
Dayal, Kaushik
Source :
Computational Materials Science. Jan2023, Vol. 217, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

We present a species-conserving Monte Carlo (MC) method, motivated by systems such as high-entropy alloys. Current fast local-structure MC methods do not conserve the net concentration of atomic species, or are inefficient for complex atomic systems. By coarse-graining the atomic lattice into clusters and developing a renormalized MC method that takes advantage of the local structure of the atoms, we are able to significantly reduce the number of iterations required for MC simulations to reach equilibrium. In addition, the structure of the method enables easy parallelizability for the future. [Display omitted] [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09270256
Volume :
217
Database :
Academic Search Index
Journal :
Computational Materials Science
Publication Type :
Academic Journal
Accession number :
160436313
Full Text :
https://doi.org/10.1016/j.commatsci.2022.111886