Machine learning for metallurgy V: A neural-network potential for zirconium data of published plots

JSON Export

{
  "revision": 5, 
  "id": "1642", 
  "created": "2023-01-30T09:08:40.594956+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:vy-02", 
    "status": "published", 
    "title": "Machine learning for metallurgy V: A neural-network potential for zirconium data of published plots", 
    "mcid": "2023.21", 
    "license_addendum": null, 
    "_files": [
      {
        "description": "The archive contains the data points for the plots published in the paper. Th files contain the .tex (LaTeX) files used to develop the plots. A description of each figure is given in README.txt.", 
        "key": "Data_Liyanage_PRM_2022.zip", 
        "size": 107844, 
        "checksum": "md5:0234c86630388b84c96950474536d9f1"
      }
    ], 
    "owner": 936, 
    "_oai": {
      "id": "oai:materialscloud.org:1642"
    }, 
    "keywords": [
      "machine learning", 
      "Zirconium", 
      "Defects", 
      "Ductility", 
      "Fracture", 
      "MARVEL"
    ], 
    "conceptrecid": "1641", 
    "is_last": true, 
    "references": [
      {
        "type": "Journal reference", 
        "doi": "10.1103/PhysRevMaterials.6.063804", 
        "url": "https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.6.063804", 
        "comment": "The archive contains the tex files used to create the plots in Figures 2, 5, 6, 7, and 8.", 
        "citation": "M. Liyanage, D. Reith, V. Eyert, and W. A. Curtin, 6, 063804 (2022)"
      }
    ], 
    "publication_date": "Jan 30, 2023, 14:40:58", 
    "license": "Materials Cloud non-exclusive license to distribute v1.0", 
    "id": "1642", 
    "description": "The mechanical performance\u2014including deformation, fracture, and radiation damage\u2014of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the nuclear industry, understanding that atomic-scale behavior is crucial. The defects controlling that performance are at size scales far larger than accessible by first-principles methods, necessitating the use of semi-empirical interatomic potentials. Existing potentials for Zr are not sufficiently quantitative, nor easily extendable to alloys, oxides, or hydrides. To overcome these issues, a neural network machine learning potential (NNP) is developed here within the Behler-Parrinello framework for Zr. With a careful choice of descriptors of the atomic environments and the creation of a first-principles training dataset that includes a wide spectrum of configurations of metallurgical relevance, a very accurate NNP is demonstrated. Specifically, the Zr NNP yields a good description of dislocation structures and their relative energies and fracture behavior, along with bulk, surface, and point-defect properties and structures, and significantly outperforms the best available traditional potentials. Results here will enable large-scale simulations of complex processes and provide the basis for future extensions to alloys, oxides, and hydrides.", 
    "version": 1, 
    "contributors": [
      {
        "email": "pandula.liyanage@epfl.ch", 
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Liyanage", 
        "givennames": "Manura"
      }, 
      {
        "affiliations": [
          "Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France"
        ], 
        "familyname": "Reith", 
        "givennames": "David"
      }, 
      {
        "affiliations": [
          "Materials Design SARL, 42 avenue Verdier, 92120 Montrouge, France"
        ], 
        "familyname": "Eyert", 
        "givennames": "Volker"
      }, 
      {
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modelling, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Curtin", 
        "givennames": "W. A."
      }
    ], 
    "edited_by": 576
  }, 
  "updated": "2023-01-30T13:40:58.692921+00:00"
}