Few-shot learning is a promising approach for addressing the challenges posed by limited data availability (typically less than 500) in ML models. In particular, fine-tuning the pre-trained models in vision or language model with only few examples can lead to high performance on unseen tasks. In this work, we applied few-shot learning to the PMTransformer. To address the issue of asymmetry data aggregation in porous materials, we propose a cross-material few-shot learning approach. This approach exploits the synergistic effects from high similarity between different classes of porous materials to improve performance. Specifically, we utilize the relatively abundant number of data for the metal-organic frameworks (MOFs) to train the PMTransformer to predict the properties of other types of porous materials. MEGNET do not exhibit a significant improvement in performance compared to the PMTransformer, as demonstrated in Supplementary Figures and.