Xin Liu^1*, Masoud Rahbar Niazi¹, Tao Liu², Binglun Yin², William Curtin¹

1 Laboratory for Multiscale Mechanics Modeling, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2 Institute of Applied Mechanics and Center for X-Mechanics, Zhejiang University, 310027 Hangzhou, China

* Corresponding authors emails: xin.liu@epfl.ch

DOI10.24435/materialscloud:3f-w3 [version v1]

Publication date: Jan 17, 2023

How to cite this record

Xin Liu, Masoud Rahbar Niazi, Tao Liu, Binglun Yin, William Curtin, A low-temperature prismatic slip instability in Mg understood using machine learning potentials, Materials Cloud Archive 2023.10 (2023), https://doi.org/10.24435/materialscloud:3f-w3

Description

Prismatic slip in magnesium at temperatures T ≲ 150 K occurs at ∼ 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 pro- cess, are shown to have an instability in prism slip at a potential-dependent critical stress. One NNP, NNP-77, 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.

Materials Cloud sections using this data

Files

File name	Size	Description
Potentials.zip MD5md5:363d7d85af875592dee8df1d45ba2c62	261.0 KiB	Neural network potentials NNP63 and NNP77
Code.zip MD5md5:2dc31d8e27817ddd3b2d3d4ce580ff76	92.8 KiB	Input files for lammps and vasp

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External references

Keywords

Magnesium Prismatic slip Neural Network potential Minimum energy path MARVEL