Neural network potential for Zr-H

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{
  "id": "2171", 
  "updated": "2024-05-03T10:04:08.592123+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Manura", 
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "email": "pandula.liyanage@epfl.ch", 
        "familyname": "Liyanage"
      }, 
      {
        "givennames": "David", 
        "affiliations": [
          "Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France"
        ], 
        "familyname": "Reith"
      }, 
      {
        "givennames": "Volker", 
        "affiliations": [
          "Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France"
        ], 
        "familyname": "Eyert"
      }, 
      {
        "givennames": "W. A.", 
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Curtin"
      }
    ], 
    "title": "Neural network potential for Zr-H", 
    "_oai": {
      "id": "oai:materialscloud.org:2171"
    }, 
    "keywords": [
      "Zirconium Hydrides", 
      "Neural network potentials", 
      "molecular dynamics simulation"
    ], 
    "publication_date": "May 03, 2024, 12:04:08", 
    "_files": [
      {
        "key": "Reference_dataset_ZrH_NNP.zip", 
        "description": "Reference structures used in developing the NNP (sharable dataset)", 
        "checksum": "md5:b042712e43d5c5b6a16bd879b3d98053", 
        "size": 56918658
      }
    ], 
    "references": [
      {
        "citation": "M. Liyanage, D, Reith, V. Eyert, W. A. Curtin, Journal of Nuclear materials", 
        "type": "Journal reference"
      }
    ], 
    "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", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "2170", 
    "is_last": true, 
    "mcid": "2024.68", 
    "edited_by": 576, 
    "id": "2171", 
    "owner": 936, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:qv-xn"
  }, 
  "revision": 2, 
  "created": "2024-05-02T16:37:49.970019+00:00"
}