Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab-initio database.

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{
  "revision": 3, 
  "id": "1914", 
  "created": "2023-09-22T13:41:03.778604+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:ya-3k", 
    "status": "published", 
    "title": "Do we really need machine learning interatomic potentials for modeling amorphous metal oxides?\nCase study on amorphous alumina by recycling an existing ab-initio database.", 
    "mcid": "2023.147", 
    "license_addendum": null, 
    "_files": [
      {
        "description": "See README.txt", 
        "key": "am_Al2O3.zip", 
        "size": 10613522, 
        "checksum": "md5:9cfff9e0a25da83e803dd8d2812bd53c"
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      {
        "description": "description for am_Al2O3.zip", 
        "key": "README.txt", 
        "size": 1377, 
        "checksum": "md5:68a87e92263dc8ad1fd49acf85ccabfd"
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    ], 
    "owner": 1143, 
    "_oai": {
      "id": "oai:materialscloud.org:1914"
    }, 
    "keywords": [
      "machine learning", 
      "molecular dynamics", 
      "MARVEL/DD1"
    ], 
    "conceptrecid": "1913", 
    "is_last": true, 
    "references": [
      {
        "type": "Journal reference", 
        "doi": "10.1088/1361-651X/ad39ff", 
        "url": "https://iopscience.iop.org/article/10.1088/1361-651X/ad39ff/meta", 
        "comment": "Publication describing the methods and discussing the results.", 
        "citation": "S.Gramatte, V. Turlo, O. Politano, Modelling Simul. Mater. Sci. Eng. 32, 045010 (2024)"
      }
    ], 
    "publication_date": "Sep 26, 2023, 12:56:43", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "1914", 
    "description": "In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive set of fixed-charge and variable-charge potentials tailored for alumina. We also train a machine learning interatomic potential, using the NequIP framework. Results highlight that the fixed-charge potential by Matsui provides an ideal blend of computational speed and alignment with ab initio findings for stoichiometric alumina. For non-stoichiometric variants, the variable charge potentials, especially ReaxFF, align remarkably well with DFT outcomes. The NequIP ML potential, while superior in some instances and adaptable, might not be the best fit for specific tasks.", 
    "version": 1, 
    "contributors": [
      {
        "email": "simon.gramatte@empa.ch", 
        "affiliations": [
          "Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, Feuerwerkerstrasse 39, 3602 Thun, Switzerland", 
          "Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Universit\u00e9 Bourgogne Franche-Comt\u00e9, 9 Avenue A. Savary, Dijon Cedex, France", 
          "Laboratory for Joining Technologies and Corrosion, Empa - Swiss Federal Laboratories for Materials Science and Technology, Ueberlandstrasse 129, 8600 Duebendorf, Switzerland"
        ], 
        "familyname": "Gramatte", 
        "givennames": "SImon"
      }, 
      {
        "email": "Vladyslav.turlo@empa.ch", 
        "affiliations": [
          "Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, Feuerwerkerstrasse 39, 3602 Thun, Switzerland"
        ], 
        "familyname": "Turlo", 
        "givennames": "Vladyslav"
      }
    ], 
    "edited_by": 1143
  }, 
  "updated": "2025-02-12T13:11:23.030173+00:00"
}