Machine-learning potentials predict orientation-and mode-dependent fracture in refractory diborides

SY Lin and Z Chen and R Janknecht and ZL Zhang and L Hultman and PH Mayrhofer and N Koutná and DG Sangiovanni, ACTA MATERIALIA, 301, 121568 (2025).

DOI: 10.1016/j.actamat.2025.121568

Fracture toughness (K-Ic) and fracture strength (sigma(f)) are key criteria in the selection and design of reliable ceramics. However, their experimental characterization remains challenging-especially for ceramic thin films, where size and interfacial effects hinder accurate and reproducible measurements. Here, machine-learning interatomic potentials (MLIPs) trained on ab initio datasets of single crystal models deformed up to fracture are used to characterize transgranular cleavage in pre-cracked ceramic diboride TMB2 (TM = Ti, Zr, Hf) lattices through stress intensity factor (K)-controlled loading. Mode-I simulations performed across distinct crack geometries show that fracture is primarily driven by straight crack extension along the original plane. The corresponding macroscale fracture-initiation properties (K-Ic approximate to 1.7-2.9 MPa . root m, sigma(f) approximate to 1.6-2.4 GPa) are extrapolated using scaling laws previously established for monocrystal ceramics. Considering TiB2 as a representative system, additional simulations explore loading conditions ranging from pure Mode-I (opening) to Mode-II (sliding). TiB2 models containing prismatic cracks exhibit their lowest fracture resistance under mixed-mode conditions, where the crack deflects onto pyramidal planes-as confirmed by nanoindentation tests on TiB2(0001) thin films. This study establishes K-controlled, MLIP-based simulations as predictive tools for orientation-and mode-dependent fracture in ceramics.

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