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

Computational Materials Science(2023)

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摘要
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.
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关键词
Clustering,Coarse graining,Local structure,Data driven
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