Machine learning of twin/matrix interfaces from local stress field

JSON Export

{
  "metadata": {
    "is_last": true, 
    "version": 1, 
    "title": "Machine learning of twin/matrix interfaces from local stress field", 
    "keywords": [
      "machine learning", 
      "molecular dynamics", 
      "magnesium twinning", 
      "MARVEL/DD1"
    ], 
    "description": "Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. The data provided here allows for reproducing findings from our work.", 
    "license": "Creative Commons Attribution 4.0 International", 
    "references": [
      {
        "type": "Preprint", 
        "citation": "J.F. Troncoso, Y. Hu, N.M. della Ventura, A. Sharma, X. Maeder, V. Turlo, ArXiv (2023)", 
        "doi": "10.48550/arXiv.2303.02247"
      }, 
      {
        "url": "https://www.sciencedirect.com/science/article/pii/S0927025623003166", 
        "type": "Journal reference", 
        "citation": "J.F. Troncoso, Y. Hu, N.M. della Ventura, A. Sharma, X. Maeder, V. Turlo, Computational Materials Science 228, 112322 (2023)", 
        "comment": "", 
        "doi": "10.1016/j.commatsci.2023.112322"
      }
    ], 
    "doi": "10.24435/materialscloud:aj-wq", 
    "conceptrecid": "1693", 
    "publication_date": "Mar 15, 2023, 19:06:29", 
    "edited_by": 969, 
    "_oai": {
      "id": "oai:materialscloud.org:1694"
    }, 
    "contributors": [
      {
        "affiliations": [
          "Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland"
        ], 
        "familyname": "Fernandez Troncoso", 
        "givennames": "Javier"
      }, 
      {
        "affiliations": [
          "Mechanics and Materials Lab, Department of Mechanical and Process Engineering, ETH Zurich, 8092, Zurich, Switzerland"
        ], 
        "familyname": "Hu", 
        "givennames": "Yang"
      }, 
      {
        "affiliations": [
          "Laboratory for Mechanics of Materials and Nanostructures, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland"
        ], 
        "familyname": "della Ventura", 
        "givennames": "Nicolo Maria"
      }, 
      {
        "affiliations": [
          "Laboratory for Mechanics of Materials and Nanostructures, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland"
        ], 
        "familyname": "Sharma", 
        "givennames": "Amit"
      }, 
      {
        "affiliations": [
          "Laboratory for Mechanics of Materials and Nanostructures, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland"
        ], 
        "familyname": "Maeder", 
        "givennames": "Xavier"
      }, 
      {
        "affiliations": [
          "Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland"
        ], 
        "email": "vladyslav.turlo@empa.ch", 
        "familyname": "Turlo", 
        "givennames": "Vladyslav"
      }
    ], 
    "owner": 969, 
    "license_addendum": null, 
    "mcid": "2023.43", 
    "_files": [
      {
        "size": 369, 
        "checksum": "md5:df6478017a8f7bbe8d925448cb37d9d4", 
        "description": "README file", 
        "key": "README.txt"
      }, 
      {
        "size": 576762380, 
        "checksum": "md5:6d9d71d1842ded7d06c8757d8d84d806", 
        "description": "Data supporting the publication", 
        "key": "data.7z"
      }
    ], 
    "id": "1694", 
    "status": "published"
  }, 
  "revision": 5, 
  "updated": "2023-11-14T12:41:11.876142+00:00", 
  "created": "2023-03-14T13:25:41.104218+00:00", 
  "id": "1694"
}