Finally, we developed another MLP model trained using the 6004 data points from the GP-PCA model. Employing the same method and hyperparameters as previously applied, we fine-tuned the MLP model to ensure consistency and comparability with our earlier methodologies. Subsequently, we utilized this MLP model to predict the adsorption of real molecules across multiple MOFs. However, upon evaluation, our findings revealed a reduction in accuracy compared to the GP-PCA model. This divergence in predictive performance underscores the intricate challenges inherent in modeling gas adsorption phenomena within MOFs using traditional MLP approaches. For instance, employing the GP-PCA model yields an R 2 value of 0.972, whereas the MLP model achieves an R 2 value of 0.922. Other real molecules predictions can be found in the SI.