You are currently on a failover version of the Materials Cloud Archive hosted at CINECA, Italy.
Click here to access the main Materials Cloud Archive.
Note: If the link above redirects you to this page, it means that the Archive is currently offline due to maintenance. We will be back online as soon as possible.
This version is read-only: you can view published records and download files, but you cannot create new records or make changes to existing ones.
Publication date: May 03, 2024
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
No Explore or Discover sections associated with this archive record.
File name | Size | Description |
---|---|---|
Reference_dataset_ZrH_NNP.zip
MD5md5:b042712e43d5c5b6a16bd879b3d98053
|
54.3 MiB | Reference structures used in developing the NNP (sharable dataset) |
2024.68 (version v1) [This version] | May 03, 2024 | DOI10.24435/materialscloud:qv-xn |