Computational material science, including irradiation physics, has been relying on classical in teratomic potentials for decades: they efficiently allow to simulate complex phenomena at length and time scales that outrun the capacity of the most accurate ab initio approaches. Two different classes of interatomic potentials are currently used. The so-called machine learning potentials[1], on one hand, have a high number of parameters and thus a high flexibility to reproduce a number of physical quantities, but a high computational cost. On the other hand, many-body interatomic potentials, like Embedded Atom Method (EAM) and tight binding Second Moment Approximation (SMA) potentials, have a more limited accuracy due to a reduced number of parameters. However, they are based on physically-inspired functional forms which ensures a reasonable transferability. Their low computational cost thus allow studies that require multiple evaluations of system en ergy and forces[2]. The identification of the potential parameters for a given system is of crucial importance, but challenging. In this work, we apply efficient tools of model screening and sensitivity analysis to get insights on the capabilities of two SMA potentials [3, 4], in the context of irradiated α-zirconium. We focus on properties that are relevant for irradiation, i.e. elastic constants, point defect properties and stacking faults energies. The local screening of the potential parameters appears to be an efficient method to check the robstness of the potential, i.e. to identify the possible artefacts related to the roughness of the potential. After correcting for these artefacts, the most influencial parameters on the computed properties are identified. In particular, one parameter affects most of the self interstitial properties, while having a limited effect on bulk and vacancy properties. This suggests a sensitivity indices-oriented technique to refit existing potentials. Following this approach, we refine a potential for irradiation defects in α-Zr, thanks to a multi-objective optimisation package.