Atomic-level insights into TiCN via machine learning force field molecular dynamics simulations

B Zhao and D Wang and SR Shi and ZW Ding and ZS Wei, JOURNAL OF ALLOYS AND COMPOUNDS, 1047, 184926 (2025).

DOI: 10.1016/j.jallcom.2025.184926

TiCN exhibits exceptional properties, making it highly promising for a range of applications; however, the absence of reliable interatomic potentials has hindered large-scale simulation studies. In this study, a deep neural network potential applicable to the Ti-C-N ternary system was developed using DeePMD-kit, enabling highaccuracy fitting of the potential energy surface based on large-scale DFT datasets. The resulting model accurately reproduces the elastic constants and volumetric responses of TiC, TiN, and TiCN systems. Further uniaxial tensile and compressive simulations demonstrate that TiCN exhibits high strength and fracture strain at low temperatures, while pronounced thermal softening and brittle failure dominate at elevated temperatures. In addition, the effect of strain rate on mechanical response is limited, whereas compressive loading reveals that moderate dislocation activity can partially improve the plastic behavior of the material. This work provides a high-accuracy potential foundation for multiscale simulations of TiCN and offers new insights into its mechanical mechanisms, thereby contributing to the design of advanced ceramic materials.

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