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Thermal stability and mechanical performance are crucial criteria in the design of next-generation protective coatings for cutting, drilling, and forming applications. Currently, TiAlN serves as the benchmark coating due to its excellent chemical, mechanical, and thermal stability. Titanium aluminium oxynitride (TiAlON), however, has emerged as a promising alternative, offering an increase in thermal stability of up to 300 °C [1].
This improvement in thermal stability is offset by a reduction in elastic modulus. We deposited TiAlON coatings by reactive high-power pulsed magnetron sputtering and observed a decrease in elastic modulus of ~20% at 15 at.% O as compared to TiAlN. In contrast, simple density-functional-theory-based models fail to capture this trend and instead predict a much larger reduction in the elastic constants, up to 50%. To address this discrepancy, we trained a machine-learning interatomic potential using the atomic cluster expansion formalism [2], enabling large-scale modelling of TiAlON. With this potential, we successfully reproduced the mechanical properties of TiAlON up to 25 at.% O in agreement with experiment. Moreover, we identified the initial stages of spinodal decomposition and the formation of vacancy–Frenkel pair complexes as the key structural features governing the elastic properties in the studied compositional range.
[1] D. M. Holzapfel et al., Acta Materialia 218, 117204 (2021).
[2] Y. Lysogorskiy et al., npj Computational Materials 7, 97 (2021).
