Manura Liyanage^1*, David Reith², Volker Eyert², W. A. Curtin¹

1 Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

2 Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France

* Corresponding authors emails: pandula.liyanage@epfl.ch

DOI10.24435/materialscloud:qv-xn [version v1]

Publication date: May 03, 2024

How to cite this record

Manura Liyanage, David Reith, Volker Eyert, W. A. Curtin, Neural network potential for Zr-H, Materials Cloud Archive 2024.68 (2024), https://doi.org/10.24435/materialscloud:qv-xn

Description

The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the Behler-Parrinello framework. The Zr-H NNP retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behavior for many different crack orientations in the most-stable ε-hydride and structures and reasonable relative energetics for the ⟨a⟩ screw dislocation in pure Zr. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles

Materials Cloud sections using this data

Files

File name	Size	Description
Reference_dataset_ZrH_NNP.zip MD5md5:b042712e43d5c5b6a16bd879b3d98053	54.3 MiB	Reference structures used in developing the NNP (sharable dataset)

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External references

Keywords

Zirconium Hydrides Neural network potentials molecular dynamics simulation