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research article

A low-temperature prismatic slip instability in Mg understood using machine learning potentials

Liu, Xin
•
Niazi, Masoud Rahbar
•
Liu, Tao
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January 15, 2023
Acta Materialia

Prismatic slip in magnesium at temperatures T <= 150 K occurs at similar to 100 MPa independent of temperature, and jerky flow due to large prismatic dislocation glide distances is observed; this athermal regime is not understood. In contrast, the behavior at T >= 150 K is understood to be governed by a thermally-activated double-cross-slip of the stable basal screw dislocation through an unstable or weakly metastable prism screw configuration and back to the basal screw. Here, a range of neural network potentials (NNPs) that are very similar for many properties of Mg including the basal-prism-basal cross-slip path and process, are shown to have an instability in prism slip at a potential-dependent critical stress. One NNP, NNP77, has a critical instability stress in good agreement with experiments and also has basal-prism-basal transition path energies in very good agreement with DFT results, making it an excellent potential for understanding Mg prism slip. Full 3d simulations of the expansion of a prismatic loop using NNP-77 then also show a transition from cross-slip onto the basal plane at low stresses to prismatic loop expansion with no cross-slip at higher stresses, consistent with in-situ TEM observations. These results reveal (i) the origin and prediction of the observed unstable low- T prismatic slip in Mg and (ii) the critical use of machine-learning potentials to guide discovery and understanding of new important metallurgical behavior.

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Type
research article
DOI
10.1016/j.actamat.2022.118490
Web of Science ID

WOS:000899523800002

Author(s)
Liu, Xin
•
Niazi, Masoud Rahbar
•
Liu, Tao
•
Yin, Binglun  
•
Curtin, W. A.  
Date Issued

2023-01-15

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

Published in
Acta Materialia
Volume

243

Article Number

118490

Subjects

Materials Science, Multidisciplinary

•

Metallurgy & Metallurgical Engineering

•

Materials Science

•

magnesium

•

prismatic slip

•

neural network potential

•

minimum energy path

•

elastic band method

•

displacement field

•

in-situ

•

1st-principles

•

glide

•

prediction

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LAMMM  
Available on Infoscience
January 16, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/193773
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