Viscosity in water from first-principles and deep-neural-network simulations


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{
  "revision": 2, 
  "metadata": {
    "publication_date": "Jun 10, 2022, 21:34:45", 
    "_oai": {
      "id": "oai:materialscloud.org:1383"
    }, 
    "license": "Creative Commons Attribution 4.0 International", 
    "description": "We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.", 
    "contributors": [
      {
        "familyname": "Malosso", 
        "affiliations": [
          "SISSA - Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy"
        ], 
        "email": "cmalosso@sissa.it", 
        "givennames": "Cesare"
      }, 
      {
        "familyname": "Zhang", 
        "affiliations": [
          "Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA", 
          "DP Technology, Beijing 100080, People\u2019s Republic of China"
        ], 
        "givennames": "Linfeng"
      }, 
      {
        "familyname": "Car", 
        "affiliations": [
          "Department of Chemistry, Department of Physics, and Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, NJ 08544, USA", 
          "Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA"
        ], 
        "givennames": "Roberto"
      }, 
      {
        "familyname": "Baroni", 
        "affiliations": [
          "SISSA - Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy", 
          "CNR Istituto Officina dei Materiali, SISSA unit, 34136 Trieste, Italy"
        ], 
        "givennames": "Stefano"
      }, 
      {
        "familyname": "Tisi", 
        "affiliations": [
          "SISSA - Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy"
        ], 
        "givennames": "Davide"
      }
    ], 
    "edited_by": 576, 
    "title": "Viscosity in water from first-principles and deep-neural-network simulations", 
    "conceptrecid": "1354", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:x7-b0", 
    "mcid": "2022.76", 
    "_files": [
      {
        "size": 564, 
        "key": "README.md", 
        "checksum": "md5:1ff3aa3ddfd7cca7c856b4031b4d6655", 
        "description": "Information on the files and instructions."
      }, 
      {
        "size": 1033, 
        "key": "generate_images.zip", 
        "checksum": "md5:5372b3d4d4912ebf219d62072dbe90d8", 
        "description": "Archive with data to generate the main plots in the paper."
      }, 
      {
        "size": 80263934, 
        "key": "dataset_PBE.zip", 
        "checksum": "md5:103af10a6e31f4289e2d24580bd7c1c2", 
        "description": "Archive with the machine-learning model and the dataset used to train it."
      }, 
      {
        "size": 264908061, 
        "key": "ab_initio-time_series.zip", 
        "checksum": "md5:1581795be9dd75a97b039ff48076c941", 
        "description": "ab initio time series of the stress series."
      }
    ], 
    "id": "1383", 
    "keywords": [
      "viscosity", 
      "molecular dynamics simulation", 
      "ab initio", 
      "machine learning", 
      "neural networks", 
      "water", 
      "transport"
    ], 
    "is_last": true, 
    "status": "published", 
    "references": [
      {
        "doi": "", 
        "url": "https://arxiv.org/pdf/2203.01262.pdf", 
        "type": "Preprint", 
        "citation": "C. Malosso, L. Zhang, R. Car, S. Baroni, D. Tisi, Viscosity in water from first-principles and deep-neural-network simulations, arXiv:2203.01262 (2022)"
      }
    ], 
    "version": 2, 
    "owner": 680
  }, 
  "id": "1383", 
  "created": "2022-06-10T13:50:50.186304+00:00", 
  "updated": "2022-06-10T19:34:45.304261+00:00"
}