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

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jats:titleAbstract</jats:title>jats:pThe prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip under mode-I loading based on empirical interatomic potentials yield contradicting predictions and artificial mechanisms. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a converged model uncertainty over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the atomistic fracture mechanism for {100} and {110} crack planes at T = 0 K, thus settling a long-standing issue. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g., nanovoids, dislocations) should be taken explicitly into account.</jats:p>


Acknowledgements: This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-2393 and EINF-3104. We thank the Centre for Information Technology of the University of Groningen (UG) for their support and for providing access to the Peregrine high performance computing cluster. L.Z. would like to thank Predrag Andric for LAMMPS implementation of fracture simulation and Miguel Caro for the implementation of Turbo SOAP descriptor. F.M. acknowledges the support from the start-up grant from the Faculty of Science and Engineering at the University of Groningen.

Funder: University of Groningen FSE Startup Grant


40 Engineering, 4016 Materials Engineering

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npj Computational Materials

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Springer Science and Business Media LLC