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Molecular dynamics simulations of radiation damage generation and dislocation loop evolution in Ni and binary Ni-based alloys

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Abstract As a great potential structural-material candidate for nuclear power applications, single-phase concentrated solid-solution alloys containing different elements have outstanding mechanical properties. In order to explore the effects of their… Click to show full abstract

Abstract As a great potential structural-material candidate for nuclear power applications, single-phase concentrated solid-solution alloys containing different elements have outstanding mechanical properties. In order to explore the effects of their elemental compositions on their radiation tolerance properties, in this work, radiation-induced defect production, as well as defect cluster and dislocation loop evolution, are investigated for equiatomic NiCo and NiFe alloys, and pure Ni, using molecular dynamics simulations. We find that the binary alloys are more radiation-resistant than pure Ni, while the NiFe binary alloy has the best radiation tolerance due to a higher recombination of defects and a lower number of surviving Frenkel pairs after cascade, compared to NiCo and pure Ni. Small defect clusters appearing in the three materials exhibit different migration behaviors, which migrate in a simple one-dimensional mode in pure Ni, but randomly change directions in the binary alloys; this behavior in the latter is thought to enhance the recombination of defects. Vacancy-type stacking fault tetrahedra and mixed interstitial dislocation loops with different types of dislocation segments are also observed. Interstitial dislocation loops generated in the NiCo and NiFe alloys show delayed and sluggish nucleation and evolution processes, which may lead to a higher probability of defect recombination during their evolution. Moreover, the formation of mixed dislocation loops is also discussed.

Keywords: evolution; molecular dynamics; dislocation; loop evolution; dislocation loop; dynamics simulations

Journal Title: Computational Materials Science
Year Published: 2020

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