Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide


JSON Export

{
  "id": "363", 
  "updated": "2020-05-28T13:51:50.781825+00:00", 
  "metadata": {
    "description": "This data set contains atomic structures of water clusters, bulk water and rock-salt Li8Mo2Ni7Ti7O32 in the XCrySDen [1] structure format (XSF), and total energies are included as additional meta information. The extended XSF format is compatible with the atomic energy network (aenet) package [2,3] for artificial neural network potential construction and application.  The structures were generated using ab initio molecular dynamics (AIMD) simulations performed with the Vienna Ab Initio Simulation Package (VASP) [4,5] and projector-augmented wave (PAW) [6] pseudopontentials.\r\n\r\nFor the bulk water system the revised Perdew-Burke-Ernzerhof density functional [7] with the Grimme D3 van-der-Waals correction [8] (revPBE+D3) was used. The AIMD simulations of the Li-Mo-Ni-Ti-O system employed the strongly constrained and appropriately normed (SCAN) semilocal density functional [9].  For both periodic systems, the plane-wave cutoff was 400 eV, and Gamma-point only k-point meshes were employed.  A time step of 1 fs was used for the integration of the equation of motion, and a Nos\u00e9-Hoover thermostat [10,11] was used to maintain the temperature at 400 K.\r\n\r\nThe energies and interatomic forces of the water cluster structures were calculated using the BLYP density functional [12,13] with additional Grimme D3 correction as implemented in the Turbomole software [14].\r\n\r\nFurther details can be found in the associated research article.\r\n\r\n[1] A. Kokalj, J. Mol. Graphics Modell. 17, 176\u2013179 (1999).\r\n[2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135\u2013150 (2016).\r\n[3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017).\r\n[4] G. Kresse, J. Furthm\u00fcller, Phys. Rev. B 54, 11169\u201311186 (1996).\r\n[5] Kresse, J. Furthm\u00fcller, Comput. Mater. Sci. 6, 15\u201350 (1996).\r\n[6] P. E. Bl\u00f6chl, Phys. Rev. B 50, 17953\u201317979 (1994).\r\n[7] Y. Zhang, W. Yang, Phys. Rev. Lett. 80, 890\u2013890 (1998).\r\n[8] S. Grimme, J. Antony, S. Ehrlich, H. Krieg, J. Chem. Phys. 132, 154104 (2010).\r\n[9] J. Sun, A. Ruzsinszky, J. Perdew, Phys. Rev. Lett. 115, 036402 (2015).\r\n[10] S. Nos\u00e9, J. Chem. Phys. 81, 511\u2013519 (1984).\r\n[11] W. G. Hoover, Phys. Rev. A 31, 1695\u20131697 (1985).\r\n[12] A. D. Becke, Phys. Rev. A 38, 3098\u20133100 (1988).\r\n[13] C. Lee, W. Yang, R. G. Parr, Phys. Rev. B 37, 785\u2013789 (1988).\r\n[14] F. Furche, R. Ahlrichs, C. H\u00e4ttig, W. Klopper, M. Sierka, F. Weigend, WIREs Comput Mol Sci 4, 91\u2013100 (2014).\r\n", 
    "contributors": [
      {
        "affiliations": [
          "Institute for Theoretical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany"
        ], 
        "givennames": "April", 
        "familyname": "Cooper"
      }, 
      {
        "affiliations": [
          "Institute for Theoretical Chemistry, University of Stuttgart, 70569 Stuttgart, Germany"
        ], 
        "givennames": "Johannes", 
        "familyname": "K\u00e4stner"
      }, 
      {
        "affiliations": [
          "Department of Chemical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA"
        ], 
        "givennames": "Alexander", 
        "familyname": "Urban"
      }, 
      {
        "email": "nartrith@atomistic.net", 
        "givennames": "Nongnuch", 
        "affiliations": [
          "Department of Chemical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA"
        ], 
        "familyname": "Artrith"
      }
    ], 
    "title": "Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide", 
    "license_addendum": "", 
    "mcid": "2020.0037/v1", 
    "id": "363", 
    "is_last": true, 
    "_oai": {
      "id": "oai:materialscloud.org:363"
    }, 
    "publication_date": "Apr 14, 2020, 00:00:00", 
    "edited_by": 81, 
    "status": "published", 
    "version": 1, 
    "license": "Creative Commons Attribution 4.0 International", 
    "_files": [
      {
        "key": "liquid-64water-AIMD-RPBE-D3-validation-data.tar.bz2", 
        "size": 11348685, 
        "description": "Independent validation set with additional structures of bulk liquid water.", 
        "checksum": "md5:dc02af62b70d980cfcfba8146788fb47"
      }, 
      {
        "key": "LMNTO-SCAN-validation-data.tar.bz2", 
        "size": 2758754, 
        "description": "Independent validation set with additional LMNTO structures.", 
        "checksum": "md5:50b330b981999815b2e0cc7af2a44292"
      }, 
      {
        "key": "water-clusters-BLYP-D3.tar.bz2", 
        "size": 2389632, 
        "description": "Structures of water clusters.", 
        "checksum": "md5:faecb1334d286e988df0bc6c475982d3"
      }, 
      {
        "key": "README.txt", 
        "size": 464053, 
        "description": "README.txt", 
        "checksum": "md5:ced800fe8a59976c484fbcb6950b1e6a"
      }, 
      {
        "key": "liquid-64water-AIMD-RPBE-D3-train-test-data.tar.bz2", 
        "size": 3761411, 
        "description": "Structures of bulk liquid water used for training and testing ANN potentials.", 
        "checksum": "md5:7faa5686b5263fa8745c2bef814220e4"
      }, 
      {
        "key": "LMNTO-SCAN-train-data.tar.bz2", 
        "size": 1267114, 
        "description": "LMNTO structures used for training ANN potentials.", 
        "checksum": "md5:2261f39e33d7844175e26d34f2b1ec86"
      }
    ], 
    "owner": 81, 
    "keywords": [
      "VASP", 
      "lithium transition metal oxide", 
      "aenet", 
      "water", 
      "AIMD"
    ], 
    "references": [
      {
        "type": "Journal reference", 
        "url": "https://doi.org/10.1038/s41524-020-0323-8", 
        "doi": "10.1038/s41524-020-0323-8", 
        "citation": "A. M. Cooper, J. K\u00e4stner, A. Urban, N. Artrith, npj Comput. Mater. 6, 54 (2020)", 
        "comment": "Peer-reviewed version of the article that makes use of this data set."
      }, 
      {
        "type": "Preprint", 
        "url": "https://arxiv.org/abs/2002.04172", 
        "doi": "", 
        "citation": "A. M. Cooper, J. K\u00e4stner, A. Urban, N. Artrith, arXiv:2002.04172 (2020)", 
        "comment": "Preprint of the article that makes use of this data set."
      }
    ], 
    "conceptrecid": "362", 
    "doi": "10.24435/materialscloud:2020.0037/v1"
  }, 
  "revision": 2, 
  "created": "2020-05-12T13:53:54.326781+00:00"
}