Additive Manufacture (AM) of high temperature Ni alloys has significant potential for the intricate cooling features found in jet engines. However, existing Ni superalloys are unsuitable for AM as they exhibit strain age cracking after manufacture. Neural networks and thermodynamic simulations have recently been used to design a new range of Ni superalloys with tailored γ and γ’ compositions that overcome this issue. High temperature lab X-Ray powder Diffraction (XRD) has been shown to be insufficient to quantify and validate this analysis. Therefore, in this study, synchrotron XRD will be used verify, optimise and refine these models by characterising the lattice mismatch between γ and γ’ in 6 of these alloys. Such insights are crucial in achieving the improved efficiencies and performance offered by structurally reliable high temperature AM parts.