Neural network potential for Zr-H
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{
"id": "2171",
"updated": "2024-05-03T10:04:08.592123+00:00",
"revision": 2,
"metadata": {
"edited_by": 576,
"contributors": [
{
"familyname": "Liyanage",
"givennames": "Manura",
"email": "pandula.liyanage@epfl.ch",
"affiliations": [
"Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
]
},
{
"familyname": "Reith",
"givennames": "David",
"affiliations": [
"Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France"
]
},
{
"familyname": "Eyert",
"givennames": "Volker",
"affiliations": [
"Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France"
]
},
{
"familyname": "Curtin",
"givennames": "W. A.",
"affiliations": [
"Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
]
}
],
"title": "Neural network potential for Zr-H",
"license": "Creative Commons Attribution 4.0 International",
"mcid": "2024.68",
"doi": "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 \u03b5-hydride and structures and reasonable relative energetics for the \u27e8a\u27e9 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",
"_files": [
{
"key": "Reference_dataset_ZrH_NNP.zip",
"checksum": "md5:b042712e43d5c5b6a16bd879b3d98053",
"description": "Reference structures used in developing the NNP (sharable dataset)",
"size": 56918658
}
],
"owner": 936,
"is_last": true,
"status": "published",
"id": "2171",
"version": 1,
"license_addendum": null,
"publication_date": "May 03, 2024, 12:04:08",
"references": [
{
"type": "Journal reference",
"citation": "M. Liyanage, D, Reith, V. Eyert, W. A. Curtin, Journal of Nuclear materials"
}
],
"keywords": [
"Zirconium Hydrides",
"Neural network potentials",
"molecular dynamics simulation"
],
"_oai": {
"id": "oai:materialscloud.org:2171"
},
"conceptrecid": "2170"
},
"created": "2024-05-02T16:37:49.970019+00:00"
}