Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime


JSON Export

{
  "metadata": {
    "is_last": true, 
    "publication_date": "Nov 16, 2021, 13:08:45", 
    "edited_by": 576, 
    "version": 1, 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "_files": [
      {
        "checksum": "md5:4f72b303307b365f3779ef917620b021", 
        "key": "nn.zip", 
        "size": 5050037, 
        "description": "Zipped TAR archive with all the necessary files to run a simulation with a neural network potential for Ni or train it from scratch"
      }
    ], 
    "mcid": "2021.192", 
    "keywords": [
      "machine learning", 
      "materials science", 
      "EPFL", 
      "metal alloys", 
      "molecular dynamics simulation", 
      "neural network potentials", 
      "electronic density of states", 
      "MARVEL/DD2", 
      "SNSF"
    ], 
    "contributors": [
      {
        "givennames": "Nataliya", 
        "email": "nataliya.lopanitsyna@epfl.ch", 
        "familyname": "Lopanitsyna", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ]
      }, 
      {
        "givennames": "Chiheb", 
        "email": "chiheb.benmahmoud@epfl.ch", 
        "familyname": "Ben Mahmoud", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ]
      }, 
      {
        "givennames": "Michele", 
        "familyname": "Ceriotti", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ]
      }
    ], 
    "status": "published", 
    "doi": "10.24435/materialscloud:vk-qd", 
    "title": "Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime", 
    "id": "772", 
    "description": "Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be obtained from experiments.  Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions.  Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations that can, therefore, be combined with extensive thermodynamic sampling.  We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost.  We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modelling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy.  The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.", 
    "owner": 344, 
    "_oai": {
      "id": "oai:materialscloud.org:772"
    }, 
    "conceptrecid": "771", 
    "references": [
      {
        "url": "https://arxiv.org/abs/2011.03874", 
        "comment": "Preprint where the neural network potential trained on the data is discussed", 
        "citation": "N. Lopanitsyna, C. Ben Mahmoud, M. Ceriotti, arXiv:2011.03874 (2020)", 
        "type": "Preprint"
      }
    ]
  }, 
  "updated": "2021-11-16T12:08:45.277632+00:00", 
  "revision": 5, 
  "id": "772", 
  "created": "2021-03-07T23:48:33.624909+00:00"
}