Thermal transport of Li₃PS₄ solid electrolytes with ab initio accuracy

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{
  "id": "2142", 
  "updated": "2024-06-16T17:56:30.950953+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Davide", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "email": "davide.tisi@epfl.ch", 
        "familyname": "Tisi"
      }, 
      {
        "givennames": "Federico", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Grasselli"
      }, 
      {
        "givennames": "Lorenzo", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Gigli"
      }, 
      {
        "givennames": "Michele", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "email": "michele.ceriotti@epfl.ch", 
        "familyname": "Ceriotti"
      }
    ], 
    "title": "Thermal transport of Li\u2083PS\u2084 solid electrolytes with ab initio accuracy", 
    "_oai": {
      "id": "oai:materialscloud.org:2142"
    }, 
    "keywords": [
      "lab-cosmo", 
      "machine learning", 
      "solid-state-electrolytes", 
      "thermal transport", 
      "EPFL", 
      "MARVEL", 
      "Swissuniversities", 
      "MaX", 
      "SNSF"
    ], 
    "publication_date": "Apr 16, 2024, 13:49:07", 
    "_files": [
      {
        "key": "MaterialsCloud-ThermalTransport.zip", 
        "description": "Folder with all the datas and scripts", 
        "checksum": "md5:71d4fd2d1183cbf55b38f02c4a28f189", 
        "size": 271730674
      }
    ], 
    "references": [
      {
        "comment": "Paper in which the data are used", 
        "doi": "10.1103/PhysRevMaterials.8.065403", 
        "citation": "D. Tisi, F. Grasselli, L. Gigli, and M. Ceriotti, Phys. Rev. Materials 8, 065403 (2024)", 
        "url": "https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.8.065403", 
        "type": "Journal reference"
      }, 
      {
        "doi": "10.48550/arXiv.2401.12936", 
        "citation": "D. Tisi, F. Grasselli, L. Gigli, and M. Ceriotti arXiv preprint arXiv:2401.12936 (2024)", 
        "url": "https://arxiv.org/abs/2401.12936", 
        "type": "Preprint"
      }
    ], 
    "description": "The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this work, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, r2SCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state-electrolyte, Li3PS4, in all its polymorphs (\u03b1, \u03b2 and \u03b3). By comparing MD estimates with lattice methods on the low-temperature, non-diffusive \u03b3-Li3PS4, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for non-diffusive phases. Finally, for the ion-conducting \u03b1 and \u03b2 phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases.", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "2141", 
    "is_last": true, 
    "mcid": "2024.57", 
    "edited_by": 766, 
    "id": "2142", 
    "owner": 766, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:nv-1g"
  }, 
  "revision": 5, 
  "created": "2024-04-16T10:28:14.999531+00:00"
}