Angular-dependent interatomic potential for large-scale atomistic simulation of iron: Development and comprehensive comparison with existing interatomic models

被引:40
|
作者
Starikov, Sergei [1 ]
Smirnova, Daria [1 ,2 ]
Pradhan, Tapaswani [1 ]
Lysogorskiy, Yury [1 ]
Chapman, Harry [3 ]
Mrovec, Matous [1 ]
Drautz, Ralf [1 ]
机构
[1] Ruhr Univ Bochum, Interdisciplinary Ctr Adv Mat Simulat ICAMS, D-44801 Bochum, Germany
[2] RAS, Joint Inst High Temp, Moscow 125412, Russia
[3] Univ Oxford, Dept Mat, Oxford OX1 3PH, England
关键词
BOUNDARY SELF-DIFFUSION; EMBEDDED-ATOM METHOD; GRAIN-BOUNDARY; MOLECULAR-DYNAMICS; AB-INITIO; INTERSTITIAL CLUSTERS; HIGH-PURITY; ALPHA-IRON; 1ST-PRINCIPLES CALCULATIONS; SCREW DISLOCATIONS;
D O I
10.1103/PhysRevMaterials.5.063607
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The development of classical interatomic potential for iron is a quite demanding task with a long history background. A new interatomic potential for simulation of iron was created with a focus on description of crystal defects properties. In contrast with previous studies, here the potential development was based on force-matching method that requires only ab initio data as reference values. To verify our model, we studied various features of body-centered-cubic iron including the properties of point defects (vacancy and self-interstitial atom), the Peierls energy barrier for dislocations (screw and mix types), and the formation energies of planar defects (surfaces, grain boundaries, and stacking fault). The verification also implies thorough comparison of a potential with 11 other interatomic potentials reported in literature. This potential correctly reproduces the largest number of iron characteristics which ensures its advantage and wider applicability range compared to the other considered classical potentials. Here application of the model is illustrated by estimation of self-diffusion coefficients and the calculation of fcc lattice properties at high temperature.
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页数:23
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    Xu, Ming
    Sun, Hong-Bo
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    Luo, Chao
    Wang, Yuhang
    Koshak, William J.
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2017, 122 (05) : 3141 - 3154
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    Rosenberg, Michael
    Taylor, Todd
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