To evaluate the performance of PMTrasnformer, we compared it with the scratch model (i.e., the default PMTransformer model without any pre-training), the MOFTransformer, which was pretrained with only MOFs, and several other baseline models, including energy histogram , descriptor-based machine learning (ML) model , and crystal graph convolutional neural network (CGCNN), using mean absolute errors (MAEs) on different properties of MOFs, COFs, PPNs, and zeolites. The evaluated properties included gas uptake, diffusivity, Henry coefficient, heat of adsorption, stability, and bandgap, as summarized in Table .