Monte Carlo study of Cu precipitation in bcc-Fe: temperature-dependent cluster expansion versus local chemical environment potentials

2021 ◽  
Vol 29 (3) ◽  
pp. 035014
Author(s):  
A Redermeier ◽  
E Kozeschnik
2016 ◽  
Vol 879 ◽  
pp. 1564-1569
Author(s):  
Alice Redermeier ◽  
Ernst Kozeschnik

In the present study, we investigate the performance of efficient pair potentials in comparison to accurate ab initio potentials as energy descriptions for Monte Carlo simulations of solid-state precipitation. As test scenario, we take the phase decomposition kinetics in binary Fe1-xCux. In a first effort, we predict thermodynamic equilibrium properties of bcc-rich Cu precipitates in an Fe-rich solution with a temperature and composition dependent Cluster Expansion. For this Cluster Expansion, combined ab inito and phonon calculations for various configurations serve as input. Alternatively, we apply the Local Chemical Environment approach, where the energy is described by computationally efficient pair potentials, which are calibrated on the first principles cluster expansion results. We observe that these fundamentally different approaches provide similar information in terms of the precipitate radius, chemical composition and interface constitution, however, the computational effort for the Local Chemical environment approach is significantly lower.


2017 ◽  
Vol 19 (39) ◽  
pp. 26606-26620 ◽  
Author(s):  
Pjotrs A. Žguns ◽  
Andrei V. Ruban ◽  
Natalia V. Skorodumova

Ordering of dopants and oxygen vacancies is studied for Gd-doped ceria (xGd ≤ 0.25) by means of a combined density functional theory (DFT) and cluster expansion approach, where the cluster interactions derived from DFT calculations are further used in Monte Carlo simulations.


2020 ◽  
Vol 22 (26) ◽  
pp. 14694-14703
Author(s):  
Hong-Tao Xue ◽  
Xu-Dong Yu ◽  
Jolyon Aarons ◽  
Fu-Ling Tang ◽  
Xue-Feng Lu ◽  
...  

Systematic cluster expansion Monte Carlo simulations of CuIn1−xAlxSe2 alloys probe the origin and evolution of In–Al segregation behavior comprehensively.


2017 ◽  
Vol 124 ◽  
pp. 17-21 ◽  
Author(s):  
Lei Ma ◽  
Riguo Mei ◽  
Mengmeng Liu ◽  
Xuxin Zhao ◽  
Qixing Wu ◽  
...  

2010 ◽  
Vol 81 (10) ◽  
Author(s):  
C. Ravi ◽  
H. K. Sahu ◽  
M. C. Valsakumar ◽  
Axel van de Walle

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.


2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll

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