Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response

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{
  "id": "2389", 
  "updated": "2024-10-15T15:35:45.692279+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Martin", 
        "affiliations": [
          "Universit\u00e9 Grenoble Alpes, 1130 Rue de la Piscine, BP 75, 38402 St Martin D'Heres, France"
        ], 
        "email": "martin.uhrin.10@ucl.ac.uk", 
        "familyname": "Uhrin"
      }, 
      {
        "givennames": "Austin", 
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "email": "austin.zadoks@epfl.ch", 
        "familyname": "Zadoks"
      }, 
      {
        "givennames": "Luca", 
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland", 
          "Department of Materials Science and Engineering, University of California at Berkeley, Berkeley, California 94720, United States", 
          "Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA"
        ], 
        "email": "lbinci@berkeley.edu", 
        "familyname": "Binci"
      }, 
      {
        "givennames": "Nicola", 
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland", 
          "Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), CH-5232 Villigen PSI, Switzerland"
        ], 
        "email": "nicola.marzari@epfl.ch", 
        "familyname": "Marzari"
      }, 
      {
        "givennames": "Iurii", 
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland", 
          "Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), CH-5232 Villigen PSI, Switzerland"
        ], 
        "email": "iurii.timrov@psi.ch", 
        "familyname": "Timrov"
      }
    ], 
    "title": "Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response", 
    "_oai": {
      "id": "oai:materialscloud.org:2389"
    }, 
    "keywords": [
      "machine learning", 
      "Hubbard corrections", 
      "density-functional theory", 
      "MARVEL"
    ], 
    "publication_date": "Oct 15, 2024, 17:35:45", 
    "_files": [
      {
        "key": "data_uv_2024_1_25.arrow", 
        "description": "Pandas dataframe store in feature format", 
        "checksum": "md5:98731288955f4e0bf13abfd6e03d54ed", 
        "size": 71344130
      }, 
      {
        "key": "README.md", 
        "description": "Gives details on what the dataset contains and how to load it in python", 
        "checksum": "md5:2d89b806e4fdfa65186afe82263ffd83", 
        "size": 4387
      }
    ], 
    "references": [
      {
        "comment": "Paper in which dataset is used for machine learning", 
        "doi": "10.48550/arXiv.2406.02457", 
        "citation": "Uhrin, M., Zadoks, A., Binci, L., Marzari, N., & Timrov, I. . arXiv 2406.02457 (2024)", 
        "url": "https://arxiv.org/abs/2406.02457", 
        "type": "Preprint"
      }
    ], 
    "description": "Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations.  This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.", 
    "status": "published", 
    "license": "Creative Commons Attribution Share Alike 4.0 International", 
    "conceptrecid": "2388", 
    "is_last": true, 
    "mcid": "2024.160", 
    "edited_by": 576, 
    "id": "2389", 
    "owner": 1523, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:r5-42"
  }, 
  "revision": 5, 
  "created": "2024-10-08T18:26:35.452153+00:00"
}