Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

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{
  "revision": 11, 
  "id": "1721", 
  "created": "2023-04-04T12:42:16.570146+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:73-yn", 
    "status": "published", 
    "title": "Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25", 
    "mcid": "2023.57", 
    "license_addendum": null, 
    "_files": [
      {
        "description": "This README describes the HEA25 dataset containing VASP outputs (HEA25.tar.gz), XYZ snapshots (HEA25.extxyz), and a Chemiscope file (HEA25.chemiscope.json.gz).", 
        "key": "README.md", 
        "size": 4309, 
        "checksum": "md5:10607febf27ef3efd677ab153a02a311"
      }, 
      {
        "description": "an XYZ format data file containing the complete dataset of approximately 25,000 structures, consisting of FCC and BCC configurations for 25 d-group elements, accompanied by their respective energies, forces, and stress tensors computed using VASP.", 
        "key": "HEA25.extxyz", 
        "size": 122071180, 
        "checksum": "md5:6c87c09e71a8ba490f7bbca648175ed6"
      }, 
      {
        "description": "a data file that can be used to generate an interactive visualization of the training data and can be loaded on http://chemiscope.org.", 
        "key": "HEA25.chemiscope.json.gz", 
        "size": 11764924, 
        "checksum": "md5:1813e7b8b6974c3abbd18e1c33df65f1"
      }, 
      {
        "description": "an archive containing some of the raw output files obtained during the calculations of the HEA25 dataset.", 
        "key": "HEA25.tar.gz", 
        "size": 10686035290, 
        "checksum": "md5:b8d734c451909cb312de5126fc8f8dfd"
      }
    ], 
    "owner": 344, 
    "_oai": {
      "id": "oai:materialscloud.org:1721"
    }, 
    "keywords": [
      "machine learning", 
      "high-entropy alloys", 
      "neural network potential", 
      "alchemical compression", 
      "MARVEL"
    ], 
    "conceptrecid": "1720", 
    "is_last": true, 
    "references": [
      {
        "type": "Preprint", 
        "doi": "10.48550/arXiv.2212.13254", 
        "url": "https://doi.org/10.48550/arXiv.2212.13254", 
        "comment": "In this reference, a comprehensive discussion on the construction of the dataset can be found.", 
        "citation": "N. Lopanitsyna, G. Fraux, M. A. Springer, S. De, and M. Ceriotti, arXiv preprint arXiv:2212.13254, (2022)."
      }
    ], 
    "publication_date": "Apr 05, 2023, 13:24:34", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "1721", 
    "description": "Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set. \nIn this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.", 
    "version": 1, 
    "contributors": [
      {
        "email": "nataliya.lopanitsyna@epfl.ch", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Lopanitsyna", 
        "givennames": "Nataliya"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Fraux", 
        "givennames": "Guillaume"
      }, 
      {
        "affiliations": [
          "BASF SE, Carl-Bosch-Stra\u00dfe 38, 67056 Ludwigshafen, Germany"
        ], 
        "familyname": "Springer", 
        "givennames": "Maximilian A."
      }, 
      {
        "affiliations": [
          "BASF SE, Carl-Bosch-Stra\u00dfe 38, 67056 Ludwigshafen, Germany"
        ], 
        "familyname": "De", 
        "givennames": "Sandip"
      }, 
      {
        "email": "michele.ceriotti@epfl.ch", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Ceriotti", 
        "givennames": "Michele"
      }
    ], 
    "edited_by": 576
  }, 
  "updated": "2023-04-05T11:24:34.244593+00:00"
}