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


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Zhang, Lei</dc:creator>
  <dc:creator>Csányi, Gábor</dc:creator>
  <dc:creator>van der Giessen, Erik</dc:creator>
  <dc:creator>Maresca, Francesco</dc:creator>
  <dc:date>2022-08-11</dc:date>
  <dc: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.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2022.102</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:ps-p7</dc:identifier>
  <dc:identifier>mcid:2022.102</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1427</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Fracture</dc:subject>
  <dc:subject>Gaussian Approximation Potential</dc:subject>
  <dc:subject>bcc iron</dc:subject>
  <dc:title>Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>