Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 132, 
    "description": "Li\u2013S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulphide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li\u2013S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe\u2013N4\u2013C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyse the adsorption trends on Zn\u2013N4\u2013C SAC and observe similar adsorption strength and behaviour with the Fe\u2013N4\u2013C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption", 
    "keywords": [
      "batteries", 
      "minima search", 
      "generalised convex hull"
    ], 
    "is_last": true, 
    "title": "Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches", 
    "status": "published", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:zz-w3", 
    "conceptrecid": "1303", 
    "_files": [
      {
        "checksum": "md5:607168f031dec8327115a05cfe6c6daf", 
        "description": "GCH Li2S notebook example", 
        "key": "LiPS-GCH_22_03_1.ipynb", 
        "size": 36190
      }, 
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        "checksum": "md5:acea76542027822b94f375640deac0bc", 
        "description": "Li2S notebook model train test and validation", 
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      }, 
      {
        "checksum": "md5:e02c63af54a4478722cc82e5754d569f", 
        "description": "training structure with energy labels", 
        "key": "FeLIPS_data.tar.gz", 
        "size": 1585005
      }, 
      {
        "checksum": "md5:495f3a8f3673b9025df6a3377442273d", 
        "description": "readme file", 
        "key": "README.txt", 
        "size": 203
      }, 
      {
        "checksum": "md5:1911f4716dab03abf4664145f6221821", 
        "description": "input file 1 CASTEP example", 
        "key": "fe1n4c66-li2s.cell", 
        "size": 3859
      }, 
      {
        "checksum": "md5:81f00f321c14eef15bf8847d8a4c7e5c", 
        "description": "input file 2 CASTEP example", 
        "key": "fe1n4c66-li2s.param", 
        "size": 586
      }
    ], 
    "references": [
      {
        "citation": "E. Lefterios, K. Rossi, ArXiV, 2112.11537 (2021)", 
        "comment": "Preprint where the data is discussed", 
        "url": "https://arxiv.org/pdf/2112.11537.pdf", 
        "doi": "10.48550/arXiv.2112.11537", 
        "type": "Preprint"
      }
    ], 
    "contributors": [
      {
        "givennames": "Eleftherios", 
        "affiliations": [
          "Department of Physics, King\u2019s College London, Strand, London, WC2R 2LS, UK"
        ], 
        "familyname": "Andritsos", 
        "email": "elefandas@gmail.com"
      }, 
      {
        "givennames": "Kevin", 
        "affiliations": [
          "Institut des Sciences et Ing\u00e9nierie Chimiques, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland"
        ], 
        "familyname": "Rossi", 
        "email": "kevin.rossi@epfl.ch"
      }
    ], 
    "_oai": {
      "id": "oai:materialscloud.org:1307"
    }, 
    "publication_date": "Apr 05, 2022, 14:56:34", 
    "mcid": "2022.48", 
    "version": 2, 
    "id": "1307", 
    "license": "Creative Commons Attribution 4.0 International"
  }, 
  "revision": 2, 
  "created": "2022-04-03T21:11:15.155065+00:00", 
  "id": "1307", 
  "updated": "2022-04-05T12:56:34.605597+00:00"
}