To this end, we resorted to principal component analysis (PCA), which is a widely employed technique to transform high-dimensional data into a lower-dimensional space, while retaining the essential patterns and structures inherent in the original data. In this study, PCA was applied to the textural properties of a larger number of MOFs (3445) than in our first approach. We used the following properties for PCA: largest pore diameter (LPD), diffusion-limiting pore diameter (DLPD), void fraction (VF), surface area (SA),