Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential

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{
  "created": "2025-03-10T20:02:48.146499+00:00", 
  "revision": 6, 
  "updated": "2025-03-11T14:10:51.726615+00:00", 
  "id": "2589", 
  "metadata": {
    "is_last": true, 
    "doi": "10.24435/materialscloud:xb-4f", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "C. Cao, A. Kingan, R. C. Hill, J. Kuang, L. Wang, C. Zhang, M. R. Carbone, H. v. Dam, S. Yoo, S. Yan, E. S. Takeuchi, K. J. Takeuchi, X. Wu, AM M. Abeykoon, A. C. Marschilok, D. Lu, submitted to PRX Energy, under review.", 
        "doi": "", 
        "type": "Journal reference"
      }
    ], 
    "description": "This database contains the neural network potential (NNP) model and training data for aqueous ZnCl\u2082 solutions from 1 m to 30 m. The NNP model can be used to compute total energies and atomic forces, with one of its major applications being large-scale molecular dynamics (MD) simulations. The model was trained using DeePMD-kit v2.2.1, with training data generated through an active learning approach implemented in DP-GEN. The energies and forces in the training set were obtained from density functional theory (DFT) calculations using the SCAN exchange-correlation functional performed using Quantum ESPRESSO. Further details on the ab initio calculation procedures and model training methodology are available in the associated manuscript (see reference below).", 
    "mcid": "2025.37", 
    "status": "published", 
    "contributors": [
      {
        "email": "ccao@bnl.gov", 
        "affiliations": [
          "Computing and Data Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA"
        ], 
        "givennames": "Chuntian", 
        "familyname": "Cao"
      }, 
      {
        "affiliations": [
          "Department of Chemistry, Princeton University, Princeton, NJ 08544, USA"
        ], 
        "givennames": "Chunyi", 
        "familyname": "Zhang"
      }, 
      {
        "affiliations": [
          "Department of Physics, Temple University, Philadelphia, PA 19122, USA"
        ], 
        "givennames": "Xifan", 
        "familyname": "Wu"
      }, 
      {
        "affiliations": [
          "Computing and Data Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA"
        ], 
        "givennames": "Matthew R.", 
        "familyname": "Carbone"
      }, 
      {
        "affiliations": [
          "Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA"
        ], 
        "givennames": "Hubertus", 
        "familyname": "van Dam"
      }, 
      {
        "affiliations": [
          "Computing and Data Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA"
        ], 
        "givennames": "Shinjae", 
        "familyname": "Yoo"
      }, 
      {
        "email": "dlu@bnl.gov", 
        "affiliations": [
          "Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA"
        ], 
        "givennames": "Deyu", 
        "familyname": "Lu"
      }
    ], 
    "edited_by": 576, 
    "_files": [
      {
        "description": "Database", 
        "key": "DPMD-model-share.zip", 
        "checksum": "md5:955bb9619df698b694c7dffd2f372850", 
        "size": 33615842
      }, 
      {
        "description": "README", 
        "key": "Readme.txt", 
        "checksum": "md5:c5a3ecd44a891bb353e8341eb4976307", 
        "size": 979
      }
    ], 
    "owner": 1112, 
    "conceptrecid": "2588", 
    "version": 1, 
    "_oai": {
      "id": "oai:materialscloud.org:2589"
    }, 
    "keywords": [
      "Neural network potential", 
      "ZnCl2 solution", 
      "water", 
      "DeePMD", 
      "water in salt"
    ], 
    "id": "2589", 
    "publication_date": "Mar 11, 2025, 15:10:51", 
    "title": "Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential"
  }
}