Simulated 3d transition metal oxides cation K-edge XANES dataset

JSON Export

{
  "id": "2383", 
  "updated": "2024-10-09T14:46:33.270503+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Shubha R.", 
        "affiliations": [
          "Brookhaven National Laboratory, Upton, New York 11973, USA"
        ], 
        "email": "skharel@bnl.gov", 
        "familyname": "Kharel"
      }, 
      {
        "givennames": "Fanchen", 
        "affiliations": [
          "Brookhaven National Laboratory, Upton, New York 11973, USA"
        ], 
        "familyname": "Meng"
      }, 
      {
        "givennames": "Xiaohui", 
        "affiliations": [
          "Brookhaven National Laboratory, Upton, New York 11973, USA"
        ], 
        "familyname": "Qu"
      }, 
      {
        "givennames": "Matthew R.", 
        "affiliations": [
          "Brookhaven National Laboratory, Upton, New York 11973, USA"
        ], 
        "email": "mcarbone@bnl.gov", 
        "familyname": "Carbone"
      }, 
      {
        "givennames": "Deyu", 
        "affiliations": [
          "Brookhaven National Laboratory, Upton, New York 11973, USA"
        ], 
        "email": "dlu@bnl.gov", 
        "familyname": "Lu"
      }
    ], 
    "title": "Simulated 3d transition metal oxides cation K-edge XANES dataset", 
    "_oai": {
      "id": "oai:materialscloud.org:2383"
    }, 
    "keywords": [
      "X-ray absorption spectroscopy", 
      "VASP", 
      "Materials"
    ], 
    "publication_date": "Oct 09, 2024, 16:46:33", 
    "_files": [
      {
        "key": "README.md", 
        "description": "Readme", 
        "checksum": "md5:4a79b36277a7bc52707a5b43bbbba478", 
        "size": 2887
      }, 
      {
        "key": "FEFF.tar.bz2", 
        "description": "FEFF materials and spectroscopy data", 
        "checksum": "md5:e7841a5642bd880080b902fe2659c5af", 
        "size": 717053618
      }, 
      {
        "key": "VASP.tar.bz2", 
        "description": "VASP materials and spectroscopy data", 
        "checksum": "md5:ce6f99e2cf9bca599d0e3dcd5b07e4fe", 
        "size": 171632699
      }
    ], 
    "references": [
      {
        "comment": "Preprint in which the data is discussed and used.", 
        "doi": "10.48550/arXiv.2409.19552", 
        "citation": "S. R. Kharel, F. Meng, X. Qu, M. R. Carbone & D. Lu. arXiv:2409.19552 (2024).", 
        "url": "https://doi.org/10.48550/arXiv.2409.19552", 
        "type": "Preprint"
      }
    ], 
    "description": "X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents with significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on simulated K-edge X-ray absorption near-edge structure (XANES) spectra database covering eight 3d transition metals (Ti-Cu). This database contains FEFF and VASP K-edge XANES spectra of 3d transition metal-containing oxide materials used to develop the above machine learning models. The materials structures are sourced from the Materials Project, corresponding to primary, secondary, ternary and quaternary oxide materials. Specifically, FEFF9 was used to compute the spectra of Ti, V, Cr, Mn, Fe, Co, Ni and Cu compounds; VASP 6.2.1 was used to compute the spectra of Ti and Cu compounds.", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "2382", 
    "is_last": true, 
    "mcid": "2024.152", 
    "edited_by": 576, 
    "id": "2383", 
    "owner": 929, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:85-8x"
  }, 
  "revision": 7, 
  "created": "2024-10-04T19:01:57.817972+00:00"
}