For the prediction of the MOF properties, the scratch model demonstrated superior performance compared to other baseline models (i.e. energy histogram, descriptor-based ML model, CGCNN) across all properties, as shown in Table . It indicates that the data representation of our model facilitates capturing the underlying feature of MOFs, leading to high performance in predicting various MOF properties. Also, the fine-tuned PMTransformer achieved lower MAE values in all of the MOF properties except for O2 uptake and N2 diffusivity compared to the MOFTransformer. This observation indicates that including other porous materials, such as COFs, PPNs, and zeolites, in the pre-training dataset of MOFTransformer leads to higher performance in predicting MOF properties, indicating synergetic effect due to similarity across all porous materials.