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

被引:4
|
作者
Fall, Aziz [1 ]
Grasinger, Matthew [2 ]
Dayal, Kaushik [1 ,3 ,4 ,5 ]
机构
[1] Carnegie Mellon Univ, Dept Mech Engn, Pittsburgh, PA 15213 USA
[2] Air Force Res Lab, Mat & Mfg Directorate, Rome, NY USA
[3] Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA USA
[4] Carnegie Mellon Univ, Ctr Nonlinear Anal, Dept Math Sci, Pittsburgh, PA USA
[5] Univ Pittsburgh, Pittsburgh Quantum Inst, Pittsburgh, PA USA
关键词
Clustering; Coarse graining; Local structure; Data driven; SIMULATION;
D O I
10.1016/j.commatsci.2022.111886
中图分类号
T [工业技术];
学科分类号
08 ;
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.
引用
收藏
页数:14
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