The dynamic diversity and invariance of ab-initio water

JSON Export

{
  "metadata": {
    "mcid": "2024.186", 
    "edited_by": 576, 
    "_files": [
      {
        "size": 645045, 
        "description": "This data repository includes all the input files for the calculation of diffusion and viscosity coefficients by machine-leaning-potential MD simulations.", 
        "key": "Inputs.zip", 
        "checksum": "md5:cb0c231a3e02cd9c7904365fc23def63"
      }
    ], 
    "id": "2451", 
    "contributors": [
      {
        "givennames": "Wei", 
        "affiliations": [
          "College of Energy, SIEMIS, Soochow University, Suzhou 215006, China"
        ], 
        "familyname": "Tian"
      }, 
      {
        "givennames": "Chenyu", 
        "affiliations": [
          "College of Energy, SIEMIS, Soochow University, Suzhou 215006, China"
        ], 
        "familyname": "Wang"
      }, 
      {
        "email": "zhouke@suda.edu.cn", 
        "affiliations": [
          "College of Energy, SIEMIS, Soochow University, Suzhou 215006, China"
        ], 
        "givennames": "Ke", 
        "familyname": "Zhou"
      }
    ], 
    "status": "published", 
    "title": "The dynamic diversity and invariance of ab-initio water", 
    "references": [
      {
        "doi": "10.1021/acs.jctc.4c01191", 
        "type": "Journal reference", 
        "citation": "Chenyu Wang, Wei Tian, and Ke Zhou*, The Dynamic Diversity and Invariance of Ab-Initio Water, Journal of Chemical Theory and Computation (2024), DOI: 10.1021/acs.jctc.4c01191", 
        "url": "https://pubs.acs.org/doi/10.1021/acs.jctc.4c01191"
      }
    ], 
    "license_addendum": null, 
    "license": "Creative Commons Attribution 4.0 International", 
    "doi": "10.24435/materialscloud:mk-fy", 
    "version": 2, 
    "_oai": {
      "id": "oai:materialscloud.org:2451"
    }, 
    "publication_date": "Nov 21, 2024, 14:53:18", 
    "keywords": [
      "water", 
      "density functional theory", 
      "machine learning potential", 
      "AIMD", 
      "dynamical properties", 
      "diffusion"
    ], 
    "description": "Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water\u2019s structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (\u03b7) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob\u2019s ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and \u03b7 adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and \u03b7 provides valuable insights for identifying the ideal temperature to accurately replicate liquid water\u2019s dynamic properties. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path toward designing better functionals for water but also underscore the significance of water\u2019s many-body characteristics.", 
    "is_last": true, 
    "conceptrecid": "2435", 
    "owner": 682
  }, 
  "revision": 3, 
  "updated": "2024-11-21T13:53:18.244075+00:00", 
  "id": "2451", 
  "created": "2024-11-21T07:24:24.693293+00:00"
}