PPNs constitute a class of porous polymers assembled from tunable building blocks through polymerization reactions, such as homocoupling of tetrahedral monomers. These reactions are typically irreversible, leading to PPNs with exceptional thermal and chemical stability, but amorphous materials. The amorphous nature presents a significant challenge for computational modeling. To address this issue, Martin et al. developed the in-silico PPN database, which utilized a crystalline modeling approach that successfully reproduced the gas adsorption behavior of PPNs. Building on this work, we constructed a diverse hypothetical PPN (hPPN) database for pre-training by PORMAKE, utilizing the same building blocks from the in-silico PPN database, but with more diverse topologies requiring nodes with four connections for tetrahedral monomers.