Ab-initio simulation of liquid water without artificial high temperature

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 682, 
    "_oai": {
      "id": "oai:materialscloud.org:2306"
    }, 
    "description": "Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water\u2019s structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, alkali earth and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab-initio simulation for investigating chemical processes in aqueous environments.", 
    "mcid": "2024.131", 
    "id": "2306", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "Chenyu Wang, Wei Tian, and Ke Zhou*, Ab-initio Simulation of Liquid Water without Artificial High Temperature, Journal of Chemical Theory and Computation (2024), in press", 
        "type": "Journal reference", 
        "url": "", 
        "doi": ""
      }, 
      {
        "citation": "Wei Tian, and Ke Zhou*, The Dynamic Diversity of Ab Initio Water, (in preparation)", 
        "type": "Journal reference"
      }
    ], 
    "doi": "10.24435/materialscloud:89-2k", 
    "keywords": [
      "water", 
      "AIMD", 
      "machine learning potential", 
      "metaGGA", 
      "density functional theory"
    ], 
    "contributors": [
      {
        "affiliations": [
          "College of Energy, SIEMIS, Soochow University, Suzhou 215006, China"
        ], 
        "familyname": "Wang", 
        "givennames": "Chenyu"
      }, 
      {
        "affiliations": [
          "College of Energy, SIEMIS, Soochow University, Suzhou 215006, China"
        ], 
        "familyname": "Tian", 
        "givennames": "Wei"
      }, 
      {
        "affiliations": [
          "College of Energy, SIEMIS, Soochow University, Suzhou 215006, China"
        ], 
        "familyname": "Zhou", 
        "email": "zhouke@suda.edu.cn", 
        "givennames": "Ke"
      }
    ], 
    "conceptrecid": "2305", 
    "version": 1, 
    "publication_date": "Aug 29, 2024, 16:21:13", 
    "is_last": true, 
    "status": "published", 
    "_files": [
      {
        "size": 124051161, 
        "checksum": "md5:1a7d02c730c0a12d3835973a1550fd2d", 
        "description": "This data repository includes training sets and neuroevolution potentials (NEPs) for liquid water, utilizing various functionals such as PBE+D3, optB88-vdW, SCAN, and rSCAN.", 
        "key": "NEP_dataset.zip"
      }
    ], 
    "title": "Ab-initio simulation of liquid water without artificial high temperature"
  }, 
  "id": "2306", 
  "updated": "2024-08-29T14:21:13.569284+00:00", 
  "created": "2024-08-13T23:57:09.183062+00:00", 
  "revision": 12
}