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

2022/08/11 by Lei Zhang, Zhang, Lei, Gábor Csányi +5 · 4 citations
Engineering · Materials Science · #FOS: Physical sciences #Hydrogen embrittlement and corrosion behaviors in metals #Materials Science (cond-mat.mtrl-sci) #Microstructure and Mechanical Properties of Steels #Microstructure and mechanical properties

paper · pdf · doi:10.48550/arxiv.2208.05912

openalex publication_date 2022/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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