Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics

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{
  "revision": 2, 
  "id": "2621", 
  "created": "2025-03-31T11:42:30.159802+00:00", 
  "metadata": {
    "doi": "10.24435/materialscloud:3h-sc", 
    "status": "published", 
    "title": "Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics", 
    "mcid": "2025.50", 
    "license_addendum": null, 
    "_files": [
      {
        "description": "Training (train.data) and test (test.data) datasets", 
        "key": "dataset.AlSi.zip", 
        "size": 362401526, 
        "checksum": "md5:4c2aa17fe6b4aceb8e15e473d5813f7d"
      }, 
      {
        "description": "input.nn, scaling.data, and weight files for the HDNNP potential of Al-Si", 
        "key": "nnp-data.AlSi.zip", 
        "size": 115459, 
        "checksum": "md5:f6bab79c3925c5f0b15d8f15c16523c5"
      }
    ], 
    "owner": 1697, 
    "_oai": {
      "id": "oai:materialscloud.org:2621"
    }, 
    "keywords": [
      "Machine learning interatomic potential", 
      "Aluminum Silicon", 
      "HDNNP"
    ], 
    "conceptrecid": "2620", 
    "is_last": true, 
    "references": [
      {
        "type": "Journal reference", 
        "citation": "Q. Bizot, N. Jakse (2025) \"(in preparation)\""
      }
    ], 
    "publication_date": "Apr 02, 2025, 18:02:18", 
    "license": "Creative Commons Attribution 4.0 International", 
    "id": "2621", 
    "description": "Solidification control is crucial in manufacturing technologies, as it determines the microstructureand,  consequently,  the performance of the final product.  Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and sub-picoseconds time scales.  Large scale molecular dynamics simulations using machinelearning interatomic potential with quantum accuracy appears the dedicated approach to complex,atomic level, multidimensional mechanisms with local symmetry breaking.  A potential trained ona high-dimensional neural network on density functional theory-based ab initio molecular dynamics(AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the  nucleation  mechanisms  occurring  at  the  early  stages  from  the  liquid  phase  near  the  eutectic composition.  Our results indicate that nucleation starts with Al at hypoeutectic compositions and with Si at hypereutectic compositions.  Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.", 
    "version": 1, 
    "contributors": [
      {
        "email": "quentin.bizot@grenoble-inp.fr", 
        "affiliations": [
          "Universit\u00e9 Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France"
        ], 
        "familyname": "Bizot", 
        "givennames": "Quentin"
      }, 
      {
        "email": "noel.jakse@grenoble-inp.fr", 
        "affiliations": [
          "Universit\u00e9 Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France"
        ], 
        "familyname": "Jakse", 
        "givennames": "Noel"
      }
    ], 
    "edited_by": 576
  }, 
  "updated": "2025-04-02T16:02:18.317839+00:00"
}