Journal Article

FZJ-2022-03837

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Data-mining of in-situ TEM experiments: On the dynamics of dislocations in CoCrFeMnNi alloys

Zhang, C.FZJ* ; Song, H.Extern* ; Oliveros, D. ; Fraczkiewicz, A. ; Legros, M. ; Sandfeld, S. (Corresponding author)FZJ*

2022
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: http://hdl.handle.net/2128/33777  doi:10.1016/j.actamat.2022.118394

Abstract: High entropy alloys are a class of materials with many significant improvements in terms of mechanical properties as compared to “classical” alloys. The corresponding structure-property relations are not yet entirely clear, but it is commonly believed that the good mechanical performance is strongly related to dislocation interactions with the complex energy landscape formed due to alloying. Although in-situ Transmission Electron Microscopy (TEM) allows high-resolution studies of the structure and dynamics of moving dislocations and makes the local obstacle/energy “landscape” directly visible in the geometry of dislocations; such observation, however, are merely qualitative, and detailed three-dimensional analyses of the interaction between dislocations and the energy landscape is still missing. In this work, we utilized dislocations as “probes” for the local energy maxima which play the role of pinning points for the dislocation movement. To this end, we developed a unique data-mining approach that can perform coarse-grained spatio-temporal analysis, making ensemble averaging of a considerable number of snapshots possible. We investigate the effect of pinning points on the dislocation gliding behavior of CoCrFeMnNi alloy during in-situ TEM straining and find that (i) the pinning point strength changes when dislocations glide through and (ii) the pinning point moves along the direction close to the Burgers vector direction. Our data-mining method can be applied to dislocation motion in general, making it a useful tool for dislocation research.

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Contributing Institute(s):

1. Materials Data Science and Informatics (IAS-9)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. MuDiLingo - A Multiscale Dislocation Language for Data-Driven Materials Science (759419) (759419)

Appears in the scientific report 2022

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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