  The 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 deformation mechanisms under mode-I loading based
on classical interatomic potentials yield contradicting predictions. 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 maximum predicted error of
8 meV/atom 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 crack
tip mechanism for {100} and {110} crack planes at T=0K, thus settling a
long-standing dispute. 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.
