Modeling the Ga/As binary system across temperatures and compositions from first principles

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{
  "metadata": {
    "title": "Modeling the Ga/As binary system across temperatures and compositions from first principles", 
    "references": [
      {
        "comment": "The paper in which the material and the results obtained with it are presented.", 
        "citation": "G. Imbalzano, M. Ceriotti, Phys. Rev. Materials 5, 063804 (2021)", 
        "doi": "10.1103/PhysRevMaterials.5.063804", 
        "url": "https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.5.063804", 
        "type": "Journal reference"
      }
    ], 
    "_files": [
      {
        "description": "Brief description of the contents of the zip folder containing the database", 
        "key": "README.txt", 
        "checksum": "md5:b18276ab4e282224fab3e2afdd46f11b", 
        "size": 1177
      }, 
      {
        "description": "The file contains the potentials, the database of structures created, examples to run MD simulations and a chemiscope visualization", 
        "key": "gaas-data.zip", 
        "checksum": "md5:6bff02119b875ce2ab436d319894a872", 
        "size": 46280341
      }, 
      {
        "description": "Chemiscope visualization of the dataset, that can be used interactively on Materials Cloud", 
        "key": "gaas-dataset.chemiscope.json.gz", 
        "checksum": "md5:c38c54f50ca040cef8681c669b3fd61a", 
        "size": 13670763
      }
    ], 
    "keywords": [
      "Molecular dynamics simulation", 
      "Gallium arsenide", 
      "Neural network potential", 
      "GaAs", 
      "SNSF", 
      "MARVEL"
    ], 
    "status": "published", 
    "mcid": "2021.226", 
    "publication_date": "Dec 20, 2021, 12:27:20", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "is_last": true, 
    "version": 2, 
    "doi": "10.24435/materialscloud:tb-dq", 
    "conceptrecid": "898", 
    "edited_by": 576, 
    "_oai": {
      "id": "oai:materialscloud.org:1188"
    }, 
    "description": "Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the Ga/As system. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committee model can be used to reliably determine the uncertainty induced by the limitations of the machine-learning model on its predictions, to identify regions of phase space that are predicted with insufficient accuracy, and to iteratively refine the training set to achieve consistent, reliable modeling.", 
    "owner": 222, 
    "contributors": [
      {
        "email": "giulio.imbalzano@epfl.ch", 
        "givennames": "Giulio", 
        "familyname": "Imbalzano", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne 1015, Switzerland"
        ]
      }, 
      {
        "email": "michele.ceriotti@epfl.ch", 
        "givennames": "Michele", 
        "familyname": "Ceriotti", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne 1015, Switzerland"
        ]
      }
    ], 
    "id": "1188"
  }, 
  "revision": 2, 
  "created": "2021-12-20T09:19:44.928434+00:00", 
  "updated": "2021-12-20T11:27:20.870743+00:00", 
  "id": "1188"
}