Machine-Learning Potentials Predict Orientation- and Mode-Dependent Fracture in Refractory Diborides
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| Format: | Preprint |
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2025
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| _version_ | 1866914068797849600 |
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| author | Lin, Shuyao Chen, Zhuo Janknecht, Rebecca Zhang, Zaoli Hultman, Lars Mayrhofer, Paul H. Koutna, Nikola Sangiovanni, Davide G. |
| author_facet | Lin, Shuyao Chen, Zhuo Janknecht, Rebecca Zhang, Zaoli Hultman, Lars Mayrhofer, Paul H. Koutna, Nikola Sangiovanni, Davide G. |
| contents | Fracture toughness ($K_\mathrm{Ic}$) and fracture strength ($σ_\mathrm{f}$) 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 \textit{ab initio} datasets of single crystal models deformed up to fracture are used to characterize transgranular cleavage in pre-cracked ceramic diboride TMB$_2$ (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 ($K_\mathrm{Ic} \approx 1.7$-2.9 MPa$\cdot\sqrt{\text{m}}$, $σ_\mathrm{f} \approx 1.6$-2.4 GPa) are extrapolated using established scaling laws. Considering TiB$_2$ as a representative system, additional simulations explore loading conditions ranging from pure Mode-I (opening) to Mode-II (sliding). TiB$_2$ 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 TiB$_2$(0001) thin films. This study establishes $K$-controlled, MLIP-based simulations as predictive tools for orientation- and mode-dependent fracture in ceramics. The approach is readily extendable to finite temperatures for evaluating fracture behavior under conditions relevant to refractory applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_18171 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-Learning Potentials Predict Orientation- and Mode-Dependent Fracture in Refractory Diborides Lin, Shuyao Chen, Zhuo Janknecht, Rebecca Zhang, Zaoli Hultman, Lars Mayrhofer, Paul H. Koutna, Nikola Sangiovanni, Davide G. Materials Science Fracture toughness ($K_\mathrm{Ic}$) and fracture strength ($σ_\mathrm{f}$) 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 \textit{ab initio} datasets of single crystal models deformed up to fracture are used to characterize transgranular cleavage in pre-cracked ceramic diboride TMB$_2$ (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 ($K_\mathrm{Ic} \approx 1.7$-2.9 MPa$\cdot\sqrt{\text{m}}$, $σ_\mathrm{f} \approx 1.6$-2.4 GPa) are extrapolated using established scaling laws. Considering TiB$_2$ as a representative system, additional simulations explore loading conditions ranging from pure Mode-I (opening) to Mode-II (sliding). TiB$_2$ 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 TiB$_2$(0001) thin films. This study establishes $K$-controlled, MLIP-based simulations as predictive tools for orientation- and mode-dependent fracture in ceramics. The approach is readily extendable to finite temperatures for evaluating fracture behavior under conditions relevant to refractory applications. |
| title | Machine-Learning Potentials Predict Orientation- and Mode-Dependent Fracture in Refractory Diborides |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.18171 |