Toward accurate prediction of N₂ uptake capacity in metal-organic frameworks

The efficient and cost-effective purification of natural gas, particularly through adsorption-based processes, is critical for energy and environmental applications. This study investigates the nitrogen (N₂) adsorption capacity across various Metal-Organic Frameworks (MOFs) using a comprehensive dataset comprising 3246 experimental measurements. To model and predict N₂ uptake behavior, four advanced machine learning algorithms-Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Gaussian Process Regression with Rational Quadratic Kernel (GPR-RQ)-were developed and evaluated. These models incorporate key physicochemical parameters, including temperature, pressure, pore volume, and surface area. Among the developed models, XGBoost demonstrated superior predictive accuracy, achieving the lowest root mean square error (RMSE = 0.6085), the highest coefficient of determination (R² = 0.9984), and the smallest standard deviation (SD = 0.60). Model performance was rigorously validated using statistical metrics and graphical analysis. Trend consistency with experimental data confirmed that XGBoost accurately captures the effect of pressure on N₂ uptake. Additionally, SHAP (Shapley Additive Explanations) analysis identified temperature as the most influential factor in adsorption prediction. Finally, an outlier assessment using the Leverage method indicated that approximately 94% of the data points were statistically valid and within the model's applicability domain.