The fine-tuned MCRT model outperformed all other models across all tasks, demonstrating both superior predictive capability and universality. ALIGNN exhibited better performance compared to other baseline models when predicting LE all (70k) and LE T2 (8k), but its performance on LE T2A (1k) does not stand out against other models. We hypothesise that this could be due to ALIGNN encoding angular information, and thus making the model more complex than other baseline models and more prone to overfitting with insufficient training samples. By contrast, MCRT, with proper pre-training, still demonstrates relatively good predictive performance even with this small dataset of 1,367 structures and energies. Graph-based models outperform SOAP descriptor-based models in predicting lattice energy, a property strongly related to the local chemical environment of atoms. Deep models on the other hand perform poorly in predicting methane capacity (MC) and diffusivity (MD) which are properties related to global structural features. A similar observation was also confirmed by studies using the MOFTransformer model . This phenomenon is further validated by the poorer performance of the purely graph-based MCRTi model. When the persistence image component is added, the MCRT model's performance improves significantly, further emphasising the importance of global geometric features in adsorption and diffusion predictions. Regarding the performance of the non-pre-trained MCRTp, it maintains a competitive performance, but is noticeably inferior to the pre-trained MCRT. The ablation model MCRTa used absolute positional embeddings as opposed to relative ones. Despite undergoing the same pre-training stage, its prediction accuracy across various tasks was consistently inferior to that of MCRT, indicating that absolute positional embedding, which does not satisfy translational and rotational invariance, indeed increases training difficulty.