In this study, our aim is to statistically analyze the arrangement of germanium (Ge) atoms across a spectrum of Si/Ge ratios, within a broad set of framework topologies (UTL, BEC, CTH, UOV, and IWW). To achieve this, we employ a methodology centred around newly developed machine learning potentials (MLPs), which drastically extend the scope of calculations, allowing for a comprehensive view of the preferential locations, distributions of Ge, and their trends across a diverse range of frameworks.