Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training scheme

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{
  "revision": 2, 
  "id": "235", 
  "created": "2020-05-12T13:53:19.507991+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:2019.0067/v1", 
    "status": "published", 
    "title": "Simulating diffusion properties of solid-state electrolytes via a neural network potential:  Performance and training scheme", 
    "mcid": "2019.0067/v1", 
    "license_addendum": "", 
    "_files": [
      {
        "description": "", 
        "key": "README.txt", 
        "size": 2319, 
        "checksum": "md5:f7f261b0382ffbc121641e2da55f1df3"
      }, 
      {
        "description": "Configurations used for training of the different materials and data to reproduce the plots.", 
        "key": "ANN_diffusion.tar.gz", 
        "size": 47870257, 
        "checksum": "md5:6f96927bdbb9a8b986f8b6591f44ad7d"
      }
    ], 
    "owner": 37, 
    "_oai": {
      "id": "oai:materialscloud.org:235"
    }, 
    "keywords": [
      "MARVEL/Inc1", 
      "Neural-network potentials", 
      "Solid-state electrolytes"
    ], 
    "conceptrecid": "234", 
    "is_last": true, 
    "references": [
      {
        "type": "Preprint", 
        "doi": "", 
        "url": "", 
        "comment": "Preprint where the data is discussed", 
        "citation": "A. Marcolongo, T. Binninger, F. Zipoli, T.Laino, arXiv:1910.10090"
      }
    ], 
    "publication_date": "Oct 25, 2019, 00:00:00", 
    "license": "Materials Cloud non-exclusive license to distribute v1.0", 
    "id": "235", 
    "description": "The recently published DeePMD model, based on a deep neural network architecture, brings the hope of solving the time-scale issue which often prevents the application of first principle molecular dynamics to physical systems. With this contribution we assess the performance of the DeePMD potential on a real-life application and model diffusion of ions in solid-state electrolytes. We consider as test cases the well known Li10GeP2S12, Li7La3Zr2O12 and Na3Zr2Si2PO12. We develop and test a training protocol suitable for the computation of diffusion coefficients, which is one of the key properties to be optimized for battery applications, and we find good agreement with previous computations. Our results show that the DeePMD model may be a successful component of a framework to identify novel solid-state electrolytes.", 
    "version": 1, 
    "contributors": [
      {
        "email": "aris.marcolongo@gmail.com", 
        "affiliations": [
          "Cognitive Computing and Computational Sciences Department, IBM Research \u2013 Zurich, Saumerstrasse 4, CH-8803 Ruschlikon, Switzerland"
        ], 
        "familyname": "Marcolongo", 
        "givennames": "Aris"
      }, 
      {
        "affiliations": [
          "Cognitive Computing and Computational Sciences Department, IBM Research \u2013 Zurich, Saumerstrasse 4, CH-8803 Ruschlikon, Switzerland"
        ], 
        "familyname": "Binninger", 
        "givennames": "Tobias"
      }, 
      {
        "affiliations": [
          "Cognitive Computing and Computational Sciences Department, IBM Research \u2013 Zurich, Saumerstrasse 4, CH-8803 Ruschlikon, Switzerland"
        ], 
        "familyname": "Zipoli", 
        "givennames": "Federico"
      }, 
      {
        "affiliations": [
          "Cognitive Computing and Computational Sciences Department, IBM Research \u2013 Zurich, Saumerstrasse 4, CH-8803 Ruschlikon, Switzerland"
        ], 
        "familyname": "Laino", 
        "givennames": "Teodoro"
      }
    ], 
    "edited_by": 98
  }, 
  "updated": "2021-12-06T13:25:22.507967+00:00"
}