Properties of α-brass nanoparticles. 1. Neural network potential energy surface

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{
  "metadata": {
    "is_last": true, 
    "version": 1, 
    "title": "Properties of \u03b1-brass nanoparticles. 1. Neural network potential energy surface", 
    "keywords": [
      "database", 
      "machine learning", 
      "neural networks", 
      "DFT", 
      "alloys", 
      "brass", 
      "potential energy surfaces"
    ], 
    "description": "**Data for Properties of \u03b1-Brass Nanoparticles. 1. Neural Network Potential Energy Surface**\nJan Weinreich, Anton R\u00f6mer, Mart\u00edn Leandro Paleico, and J\u00f6rg Behler\n\n53\u202f841 reference structures of alpha brass (less 40 % Zn) with following split\n - 4009 brass clusters\n - 8492 molten brass bulk structures\n - 8964 copper slabs, and 16\u202f878 brass slabs\n - 5377 copper bulk structures\n - 10\u202f121 brass bulk structures have been included. \n\n53\u202f841 total energies and 8\u202f903\u202f340 force components. The ranges of values for the energies and force components to be fitted have a width of about 2 eV/atom and 15 eV/\u00c5, respectively. However, some structures may have slightly higher Zn content as discussed in Fig 3 (https://arxiv.org/abs/2001.10906)\n\nThe archive contains an easily usable npz file as well as the original input.data file used to fit the potential energy surface. Additionally a Jupyter notebook describes in great detail how the data was converted to the npz format and how to read the data e.g. for subsequent use with python. In addition an example VASP calculation was added to provide detailed information about how the reference data was calculated with DFT.\n\nDETAILS:\n- DFT PB VASP-5.3 target accuracy of the total energy few meV/atom\n- Convergence tests with respect to the number of k-points showed that in order to fulfill this criterion a k-point grid of 12 \u00d7 12 \u00d7 12 is needed for a conventional four-atom copper fcc unit cell with a lattice constant of about 3.63 \u00c5 along with a plane wave cutoff energy of 500 eV and projector augmented wave potentials.(61,63)\n- Larger systems have been calculated using an adapted k-point grid corresponding to the same k-point density. The \u0393-point centered k-point grids have been constructed employing the Monkhorst\u2013Pack scheme. For surface calculations, 4\u201314 layer slabs with a total vacuum thickness of at least 8 \u00c5 have been used. \n- In the case of cluster calculations, which have also been treated in a periodic setup, the periodic images of the clusters have been separated by at least 8 \u00c5 in all three spatial directions. The convergence of very large clusters with diameters of d \u2248 22 \u00c5, which have been used to include specific atomic environments in the data set, has been extensively tested and we found that using the \u0393-point only is sufficient to reach the required convergence level.", 
    "license": "Creative Commons Attribution 4.0 International", 
    "references": [
      {
        "url": "https://pubs.acs.org/doi/10.1021/acs.jpcc.0c00559", 
        "type": "Journal reference", 
        "citation": "J. Weinreich, A. R\u00f6mer, M. L. Paleico, J. Behler, The Journal of Physical Chemistry C 2020, 124, 23, 12682-12695", 
        "comment": "you may also read the preprint version: https://arxiv.org/abs/2001.10906", 
        "doi": "10.1021/acs.jpcc.0c00559"
      }, 
      {
        "url": "https://pubs.acs.org/doi/10.1021/acs.jpcc.1c02314", 
        "type": "Journal reference", 
        "citation": "J. Weinreich, M. L. Paleico, J. Behler, The Journal of Physical Chemistry C 2021, 125, 27, 14897\u201314909", 
        "comment": "you may also read the arxiv version: https://arxiv.org/abs/2103.14130", 
        "doi": "10.1021/acs.jpcc.1c02314"
      }
    ], 
    "doi": "10.24435/materialscloud:94-aq", 
    "conceptrecid": "1010", 
    "publication_date": "Sep 26, 2021, 15:03:49", 
    "edited_by": 100, 
    "_oai": {
      "id": "oai:materialscloud.org:1011"
    }, 
    "contributors": [
      {
        "affiliations": [
          "Institut f\u00fcr Physikalische Chemie, University of G\u00f6ttingen, Germany"
        ], 
        "email": "jan.weinreich@univie.ac.at", 
        "familyname": "Weinreich", 
        "givennames": "Jan"
      }, 
      {
        "affiliations": [
          "Institut f\u00fcr Physikalische Chemie, University of G\u00f6ttingen, Germany"
        ], 
        "email": "martin.paleico@mpibpc.mpg.de", 
        "familyname": "Leandro Paleico", 
        "givennames": "Mart\u00edn"
      }, 
      {
        "affiliations": [
          "Institut f\u00fcr Physikalische Chemie, University of G\u00f6ttingen, Germany"
        ], 
        "email": "anton.roemer@stud.uni-goettingen.de", 
        "familyname": "Roemer", 
        "givennames": "Anton"
      }
    ], 
    "owner": 511, 
    "license_addendum": null, 
    "mcid": "2021.153", 
    "_files": [
      {
        "size": 232360070, 
        "checksum": "md5:f88a44e5d69b02f30d68aeadfaeae878", 
        "description": "Brass structures, energies and forces from DFT used to fit a potential energy surface using a neural network potential & example DFT calculation with VASP input used", 
        "key": "brass_DFT_data.zip"
      }
    ], 
    "id": "1011", 
    "status": "published"
  }, 
  "revision": 8, 
  "updated": "2021-09-26T13:03:49.948930+00:00", 
  "created": "2021-09-12T12:49:43.343675+00:00", 
  "id": "1011"
}