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  5. Ab initio modeling of the energy landscape for screw dislocations in body-centered cubic high-entropy alloys

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Article
English
2020

Ab initio modeling of the energy landscape for screw dislocations in body-centered cubic high-entropy alloys

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English
2020
npj Computational Materials
Vol 6 (1)
DOI: 10.1038/s41524-020-00377-5

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Robert O. Ritchie
Robert O. Ritchie

University of California, Berkeley

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Sheng Yin
Jun Ding
Mark Asta
+1 more

Abstract

In traditional body-centered cubic (bcc) metals, the core properties of screw dislocations play a critical role in plastic deformation at low temperatures. Recently, much attention has been focused on refractory high-entropy alloys (RHEAs), which also possess bcc crystal structures. However, unlike face-centered cubic high-entropy alloys (HEAs), there have been far fewer investigations into bcc HEAs, specifically on the possible effects of chemical short-range order (SRO) in these multiple principal element alloys on dislocation mobility. Here, using density functional theory, we investigate the distribution of dislocation core properties in MoNbTaW RHEAs alloys, and how they are influenced by SRO. The average values of the core energies in the RHEA are found to be larger than those in the corresponding pure constituent bcc metals, and are relatively insensitive to the degree of SRO. However, the presence of SRO is shown to have a large effect on narrowing the distribution of dislocation core energies and decreasing the spatial heterogeneity of dislocation core energies in the RHEA. It is argued that the consequences of the mechanical behavior of HEAs is a change in the energy landscape of the dislocations, which would likely heterogeneously inhibit their motion.

How to cite this publication

Sheng Yin, Jun Ding, Mark Asta, Robert O. Ritchie (2020). Ab initio modeling of the energy landscape for screw dislocations in body-centered cubic high-entropy alloys. npj Computational Materials, 6(1), DOI: 10.1038/s41524-020-00377-5.

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Publication Details

Type

Article

Year

2020

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

npj Computational Materials

DOI

10.1038/s41524-020-00377-5

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