Pure Magnesium DFT calculations for interatomic potential fitting

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{
  "revision": 4, 
  "id": "605", 
  "created": "2020-10-15T05:11:43.538238+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:8f-1s", 
    "status": "published", 
    "title": "Pure Magnesium DFT calculations for interatomic potential fitting", 
    "mcid": "2020.129", 
    "license_addendum": "", 
    "_files": [
      {
        "description": "Dataset of VASP inputs and calculations.", 
        "key": "Mg_DFT_mechanical.tar.xz", 
        "size": 37288700, 
        "checksum": "md5:5584f25ba4e7edbcc638ae78ce2a2285"
      }, 
      {
        "description": "The neural-network potential for magnesium.", 
        "key": "NNP63.tar.xz", 
        "size": 122164, 
        "checksum": "md5:95d17a09cee6f6820a3cb31b61a7a1a9"
      }, 
      {
        "description": "README file.", 
        "key": "README.txt", 
        "size": 1413, 
        "checksum": "md5:42201d64c5a376c308d45f939103694a"
      }
    ], 
    "owner": 18, 
    "_oai": {
      "id": "oai:materialscloud.org:605"
    }, 
    "keywords": [
      "DFT", 
      "magnesium", 
      "metallurgy", 
      "stacking faults", 
      "decohesion", 
      "surfaces", 
      "elasticity", 
      "MARVEL/DD2", 
      "SNSF", 
      "EPFL"
    ], 
    "conceptrecid": "380", 
    "is_last": true, 
    "references": [
      {
        "type": "Journal reference", 
        "doi": "10.1103/PhysRevMaterials.4.103602", 
        "url": "https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.4.103602", 
        "comment": "", 
        "citation": "M. Stricker, B. Yin, E. Mak, and W. A. Curtin, Physical Review Materials, 4, 103602 (2020)"
      }
    ], 
    "publication_date": "Oct 22, 2020, 10:44:25", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "605", 
    "description": "This dataset provides DFT (density functional theory as implemented in VASP, Vienna Ab Initio Simulation Package) calculations for pure Magnesium. It was designed by Binglun Yin, Markus Stricker and William A. Curtin for fitting a neural network potential with Behler-Parrinello symmetry functions. Binglun Yin carried out the calculation.\n\nIt corresponds to a dataset that is commonly used to fit interatomic potentials for mechanics applications and includes structure-energy relationships for structures used to calculate:\n1. Bulk properties\n2. Generalized stacking fault energies\n3. Decohesion and relaxed surfaces\n4. Dimer\n5. Corner and rod geometries\n6. Vacancy formation energy", 
    "version": 2, 
    "contributors": [
      {
        "email": "binglun.yin@epfl.ch", 
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modeling, Institute of Mechanical Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Yin", 
        "givennames": "Binglun"
      }, 
      {
        "email": "markus.stricker@epfl.ch", 
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modeling, Institute of Mechanical Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Stricker", 
        "givennames": "Markus"
      }, 
      {
        "email": "william.curtin@epfl.ch", 
        "affiliations": [
          "Laboratory for Multiscale Mechanics Modeling, Institute of Mechanical Engineering, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Curtin", 
        "givennames": "W. A."
      }
    ], 
    "edited_by": 100
  }, 
  "updated": "2020-10-22T08:44:25.717264+00:00"
}