Ab initio thermodynamics of liquid and solid water: supplemental materials

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{
  "revision": 2, 
  "id": "71", 
  "created": "2020-05-12T13:52:24.409564+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:2018.0020/v1", 
    "status": "published", 
    "title": "Ab initio thermodynamics of liquid and solid water: supplemental materials", 
    "mcid": "2018.0020/v1", 
    "license_addendum": "", 
    "_files": [
      {
        "description": "An overview of the data set.", 
        "key": "NOTE", 
        "size": 478, 
        "checksum": "md5:66083ac5ed9489b4a62f0506c24f4a2c"
      }, 
      {
        "description": "The parameters of the water neural network potential based on revPBE0-D3 DFT, and an example on how to use it.", 
        "key": "NN-potential.zip", 
        "size": 22723, 
        "checksum": "md5:bcca65b0296e8916615c5c517c9998ee"
      }, 
      {
        "description": "A whole set of input files for running\r\n* path-integral molecular dynamics simulations   ./pimd/\r\n* Free energy estimation of an ice system using thermodynamic integration method using the NN potential ./NN-TI/\r\n* revPBE0-D3 DFT calculations using the CP2K code ./cp2k-input/\r\n* compute the chemical potential difference between ice and liquid water using the interface pinning method ./interface-pinning/\r\n* Thermodynamic integration between the MBPOL water potential and the neural network potential ./mbpol-TI/\r\n* a sample python data analysis notebook ./data-analysis/", 
        "key": "input-files.zip", 
        "size": 7755156, 
        "checksum": "md5:57f26ce2a2e717e1ddc66caddde20700"
      }, 
      {
        "description": "The training set for ML potentials, based on revPBE0-D3 DFT.\r\n1593 bulk liquid water configurations +  energy + forces\r\n* input.data: the format for training neural network potentials. \r\n* dataset_1593.xyz:  in libatom format. ", 
        "key": "training-set.zip", 
        "size": 18245690, 
        "checksum": "md5:8cf0da8a72ddcb778529d2869990a53c"
      }
    ], 
    "owner": 56, 
    "_oai": {
      "id": "oai:materialscloud.org:71"
    }, 
    "keywords": [
      "Machine learning potential", 
      "Machine learning potential training set", 
      "Free energy calculation input files"
    ], 
    "conceptrecid": "70", 
    "is_last": true, 
    "references": [
      {
        "type": "Preprint", 
        "doi": "", 
        "url": "https://arxiv.org/abs/1811.08630", 
        "comment": "Preprint where the data is discussed. The manuscript is also accepted in PNAS.", 
        "citation": "B. Cheng, E. A. Engel, J. Behler, C. Dellago, and M. Ceriotti.  arXiv preprint arXiv:1811.08630 (2018)."
      }
    ], 
    "publication_date": "Dec 04, 2018, 00:00:00", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "71", 
    "description": "Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step.\r\n\r\nIn this set of supplemental materials, we have included the neural network potential for bulk water, including its training set in two different formats. We have also included the input files for running free energy calculations.", 
    "version": 1, 
    "contributors": [
      {
        "email": "bingqing.cheng@epfl.ch", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling, Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Cheng", 
        "givennames": "Bingqing"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling, Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Engel", 
        "givennames": "Edgar"
      }, 
      {
        "affiliations": [
          "Universit\u00e4t G\u00f6ttingen, Institut f\u00fcr Physikalische Chemie, Theoretische Chemie, Tammannstr. 6, 37077 G\u00f6ttingen, Germany"
        ], 
        "familyname": "Behler", 
        "givennames": "J\u00f6rg"
      }, 
      {
        "affiliations": [
          "Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria"
        ], 
        "familyname": "Dellago", 
        "givennames": "Christoph"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling, Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Ceriotti", 
        "givennames": "Michele"
      }
    ], 
    "edited_by": 98
  }, 
  "updated": "2021-12-06T13:56:56.097426+00:00"
}