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

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <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: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:publisher>Materials Cloud</dc:publisher>
  <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
  <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>