This proposal seeks to investigate the effect of atomic vacancies on the dynamical stability of niobium nitride (NbN), a material of interest for superconducting applications and hard coatings. While the properties of NbN are well-documented, the role of vacancies in affecting its dynamical stability remains insufficiently understood. Atomic vacancies can alter the vibrational modes of the crystal lattice, potentially leading to phase transitions or instabilities that impact the mechanical, thermal, and superconducting properties. The research will utilize first-principles calculations, density functional theory (DFT), and phonon dispersion analysis to systematically study how varying concentrations and distributions of vacancies in NbN influence its dynamical stability. This study aims to identify critical vacancy thresholds that may induce soft phonon modes or other destabilizing effects, which are important for understanding and predicting the behavior of NbN under different conditions. The data obtained from this investigation will also be used to develop machine learning potentials, which can significantly enhance the efficiency of future calculations and simulations involving NbN and similar materials. This approach will provide a foundation for further studies on defect-induced behaviors in NbN, contributing to a more nuanced understanding of its stability and guiding the design of materials with tailored properties for specific applications.