Finally, we construct a GPR model using the 611 MOFs that are common across all selection methods. These MOFs encompass a wide range of structural and property diversity. To ensure we are consistent with 5000 training data points, we focus on the eight most frequently observed pressure points (see Figure ) in the dataset which amounts to 4888 training data points in this case. The primary objective of this step is to develop a GPR model capable of accurately predicting adsorption performance while maintaining generalizability and consistency across diverse material and pressure conditions. By integrating these strategically selected MOFs and pressure points, we aim to establish a final adsorption predictive framework that combines the strengths of all the methods explored earlier.