Figure (a) shows a representative porous materials input data representations for two disparate features (i.e., local features and global features), which serve as inputs of PMTransformer. The local features involve atomistic information related to chemistry of building blocks and specific bonds. The output features of crystal graph convolutional neural networks (CGCNN) were adopted to describe the local features given that they enable capturing atoms' neighbor information such as atom types, distances between neighbor atoms. On the other hand, the global features represent crystalline features including topological and geometric descriptors such as pore volume, surface area, which are captured by the 3D energy grids. The grids are created by calculating interaction energy between a structure and a gas molecules (or gas probe) at each grid point, and can be treated as 3D images, thereby leading to understand the global features. Similar to the Vision Transformer, energy grids are divided by 6 x 6 X 6 patches and flattened by a linear projection. Finally, the local and global embedding are fed into the Transformer encoder of PMTransformer. COFs are constructed from organic building blocks with different topologies, linked with covalent bonds. The organic building blocks are relatively rigid backbones that endow the COFs with crystallinity, making them distinct from organic polymers with low crystallinity. The COFs can be synthesized using reactions of boron, triazine, and imine condensation. Various databases of synthesized COFs, including the CURATED COF and CoRE COF databases, as well as hypothetical databases, have been established. For example, Lan et al. developed the Genomic COF database, which contains 471,990 COFs constructed from 130 genetic structure units (GSUs) consisting of 58 centers, 64 linkers, and 8 functional groups with 24 topologies, using reactive sites and quasi-reactive assembly algorithms (QReaxAA). For the pre-training dataset, we constructed a hypothetical COF (hCOF) database using the 130 GSUs and topologies registered in the RCSR database. As shown in Figure , we generated 519,606 COFs by PORMAKE, of which only 747 topologies met the constraints with a root mean squared deviation (RMSD) of atomic positions between the building blocks and target node position to measure the strain energy less than 0.3. Notably, the large hCOF dataset, containing numerous COF structures with diverse topologies, enables the PMTransformer to achieve a superior understanding of COFs during pretraining stages.