Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential


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{
  "revision": 10, 
  "metadata": {
    "publication_date": "Aug 11, 2022, 21:22:28", 
    "_oai": {
      "id": "oai:materialscloud.org:1427"
    }, 
    "license": "Creative Commons Attribution 4.0 International", 
    "description": "Existing, classical interatomic potentials for bcc iron predict contradicting crack-tip mechanisms (i.e. cleavage, dislocation emission, phase transition) for the same crack systems, thus leaving the crack propagation mechanism in bcc iron unclear. In this work, we develop a Gaussian approximation potential (GAP) by extending a DFT database for ferromagnetic bcc iron to include highly distorted primitive bcc cells and surface separation, along with small crack-tip configurations that are identified by means of a fully automated active learning workflow. Our GAP (referred to as Fe-GAP22) predicts crack propagation within 8 meV/atom accuracy. The fully automated, active learning workflow is made publicly available on GitHub. With the newly developed Fe-GAP22, we find that in absence of other defects around the crack tip (e.g. nanovoids, dislocations), the static (T=0K) crack-tip mechanism is cleavage, thus settling the contradictions in the literature. Our work also highlights the need for multi-scale modelling to predict fracture at finite temperatures and finite strain rates.", 
    "contributors": [
      {
        "familyname": "Zhang", 
        "affiliations": [
          "Engineering and Technology Institute, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands"
        ], 
        "email": "lei.zhang@rug.nl", 
        "givennames": "Lei"
      }, 
      {
        "familyname": "Cs\u00e1nyi", 
        "affiliations": [
          "Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom"
        ], 
        "givennames": "G\u00e1bor"
      }, 
      {
        "familyname": "van der Giessen", 
        "affiliations": [
          "Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands"
        ], 
        "givennames": "Erik"
      }, 
      {
        "familyname": "Maresca", 
        "affiliations": [
          "Engineering and Technology Institute, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands"
        ], 
        "email": "f.maresca@rug.nl", 
        "givennames": "Francesco"
      }
    ], 
    "edited_by": 806, 
    "title": "Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential", 
    "conceptrecid": "1426", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:ps-p7", 
    "mcid": "2022.102", 
    "_files": [
      {
        "size": 4634486, 
        "key": "GAP22.zip", 
        "checksum": "md5:8a370b53981b2fe7caba6f13a46da1d4", 
        "description": "GAP potential file and DFT database"
      }, 
      {
        "size": 2264, 
        "key": "train.in", 
        "checksum": "md5:b923b10134658e8f7c36960ca5bad9a4", 
        "description": "QUIP training commands of current Fe-GAP22"
      }
    ], 
    "id": "1427", 
    "keywords": [
      "Fracture", 
      "Gaussian Approximation Potential", 
      "bcc iron"
    ], 
    "is_last": true, 
    "status": "published", 
    "references": [
      {
        "doi": "10.48550/arXiv.2208.05912", 
        "url": "https://doi.org/10.48550/arXiv.2208.05912", 
        "type": "Preprint", 
        "citation": "L. Zhang, G. Cs\u00e1nyi, E. van der Giessen, F. Maresca, arXiv:2208.05912v1"
      }
    ], 
    "version": 1, 
    "owner": 806
  }, 
  "id": "1427", 
  "created": "2022-08-01T19:26:00.577014+00:00", 
  "updated": "2022-09-01T14:04:34.112421+00:00"
}