Unraveling the crystallization kinetics of the Ge₂Sb₂Te₅ phase change compound with a machine-learned interatomic potential

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{
  "id": "2005", 
  "updated": "2024-02-22T08:57:09.285160+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Omar", 
        "affiliations": [
          "Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, Milano, I-20125, Italy."
        ], 
        "familyname": "Abou El Kheir"
      }, 
      {
        "givennames": "Luigi", 
        "affiliations": [
          "Atomistic Simulations, Italian Institute of Technology, Via E. Melen 83, Genova, I-16152, Italy."
        ], 
        "familyname": "Bonati"
      }, 
      {
        "givennames": "Michele", 
        "affiliations": [
          "Atomistic Simulations, Italian Institute of Technology, Via E. Melen 83, Genova, I-16152, Italy."
        ], 
        "familyname": "Parrinello"
      }, 
      {
        "givennames": "Marco", 
        "affiliations": [
          "Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, Milano, I-20125, Italy."
        ], 
        "email": "marco.bernasconi@unimib.it", 
        "familyname": "Bernasconi"
      }
    ], 
    "title": "Unraveling the crystallization kinetics of the Ge\u2082Sb\u2082Te\u2085 phase change compound with a machine-learned interatomic potential", 
    "_oai": {
      "id": "oai:materialscloud.org:2005"
    }, 
    "keywords": [
      "Neural Network Potential", 
      "Crystallization", 
      "Phase Change Memories", 
      "Ge2Sb2Te5", 
      "Phase Change Materials", 
      "Molecular Dynamics Simulation"
    ], 
    "publication_date": "Feb 12, 2024, 13:44:40", 
    "_files": [
      {
        "key": "NN_potential.zip", 
        "description": "DeepMD potential for Ge\u2082Sb\u2082Te\u2085", 
        "checksum": "md5:567a33cd8189751fd47d908cc1969f10", 
        "size": 8324665
      }, 
      {
        "key": "DB.zip", 
        "description": "The DFT database used to fit the NN potential divided into cubic, hexagonal, and disordered configurations (i.e. liquid, supercooled liquid, and amorphous phases). The disordered configurations are furtherly divided into configurations from DFT MD simulations, from NN MD simulations, and NN metadynamics simulations. The database is provided in DeepMD format.", 
        "checksum": "md5:d631b0621c47ceb5802f831069166842", 
        "size": 699857014
      }, 
      {
        "key": "Crystallization_trajectories.zip", 
        "description": "Trajectories files of the homogeneous crystallization at 600 K and heterogeneous crystallization at 610 K", 
        "checksum": "md5:8b7b813879efc2749e2ffef45a4defb3", 
        "size": 307725253
      }, 
      {
        "key": "crystallization_610K.mp4", 
        "description": "Video of heterogeneous crystallization dynamics at 610 K", 
        "checksum": "md5:2e46bd32d6f83a4849650dfa6a27ef3c", 
        "size": 17044546
      }
    ], 
    "references": [
      {
        "doi": "https://doi.org/10.1038/s41524-024-01217-6", 
        "citation": "O. Abou El Kheir, L. Bonati, M. Parrinello, M. Bernasconi, npj Comput Mater 10, 33 (2024).", 
        "type": "Journal reference"
      }
    ], 
    "description": "The phase change compound Ge\u2082Sb\u2082Te\u2085 (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within Density Functional Theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices.", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "2004", 
    "is_last": false, 
    "mcid": "2024.27", 
    "edited_by": 576, 
    "id": "2005", 
    "owner": 1200, 
    "license_addendum": "except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.", 
    "doi": "10.24435/materialscloud:a8-45"
  }, 
  "revision": 21, 
  "created": "2023-12-01T11:36:57.928996+00:00"
}