## 1 Introduction  
Transition metal nitrides like niobium nitride (NbN), zirconium nitride (ZrN), hafnium nitride (HfN), and titanium nitride (TiN) are recognized for their structural strength and exhibit superconducting properties surpassing the critical temperature of liquid helium (4.2 K) [1]. Discovered by [PERSON] et al. in 1941 [2], NbN is a top candidate for cryoelectronic applications due to higher superconducting transition temperature (17 K) [3; 4; 5] and stability [6]. Experiments investigating the replacement of other transition metals, such as titanium and tantalum, for niobium have been conducted in an attempt to enhance the superconducting properties of niobium nitride [7; 8; 9; 10]. One of the earlier attempts was made by [PERSON] et al. using a high-purity reactive sputtering process on a collection of Nb-Ti-N films with varying Nb/Ti ratios, achieving the highest transition temperature (\(\sim\)15.5 K) at a Nb\({}_{0.5}\)Ti\({}_{0.5}\)N composition [11]. Notably, this pioneering study revealed that incorporating Ti resulted in decreased critical current (Jc) and critical magnetic field (Hc2) in thin-film samples [11] of NbN, a finding in contrast to other reports that suggested potential enhancements in superconducting properties with similar modifications (Figure 1). Niobium titanium nitride (NbTiN) is a noteworthy variation that shares material properties and superconducting uses with NbN [9; 10; 12]. Through Ti integration, NbTiN improves device efficiency while retaining a Tc that is equivalent to NbN. Compared to NbN, NbTiN displays lower resistivity at room temperature and material characteristics that are less sensitive to the subtleties of the deposition environment [12; 13]. NbTiN is attractive for superconducting radio-frequency (SRF) applications because of its low secondary electron emission coefficient and radiation damage resistance, both of which help to achieve greater accelerating fields [14]. In addition, NbTiN has been shown to have a lower surface resistance than NbN, meaning that losses in superconductor-insulator-superconductor (SIS) mixers would be reduced [15]. NbTiN provides advantages for superconducting nanowire single-photon detectors (SNSPDs) such as a reduction in the dark count rate and preservation of the NbN detector's quantum efficiency, which improves the detector's overall performance and signal-to-noise ratio [13]. Several research works have examined the behavior of NbTiN devices and examined the consequences of adding a new element to the NbN matrix. However, the final property is not dependent solely on composition but also on the manufacturing process [3; 9; 10; 11; 14]. Manually exploring the wide range of potential elemental additions and their corresponding concentrations is a time-consuming process, and to accelerate the identification of target experiments, computational methods and machine learning approaches are valuable. There is a growing impetus to develop innovative approaches, with machine learning emerging as a powerful ally capable of managing complex datasets and transcending disciplinary borders to enhance the discovery and characterization of potential superconductors [16; 17; 18; 19]. Although the earlier findings provided mixed results regarding the benefits of Ti substitution, they opened avenues for further exploration using advanced tools and methodologies. By employing a data-driven approach, we aim to systematically evaluate the effects of substituting various transition metals, such as Ti, Ta, Zr, Hf, Re, and Sc, for niobium within the NbN matrix. We previously developed a machine learning framework to optimize key processing parameters, such as film thickness, substrate chemistry, and deposition conditions, to enhance the superconducting transition temperature of niobium nitride films. This approach resulted in a predicted 16.4% increase in Tc, demonstrating that superconducting performance is significantly influenced by both the material form (bulk or films) and the specific processing conditions.