Machine learning assisted predictions for hydrogen storage in metal-organic frameworks

Metal organic frameworks (MOFs) have been studied vastly for hydrogen storage purposes due to their unique properties. In the present work hydrogen storage capacity is modeled using machine learning approaches including Multi-layer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), CatBoost, LightGBM, XGBoost, and Committee Machine Intelligence System (CMIS) trained on experimental data gathered from various experimental studies. CMIS model shows the best results with R2=0.982 and Root Mean Square Error = 0.088. Additionally, this study uses the Leverage method to detect and eliminate suspected data points in order to improve predictability. Only 2.04% of data points are detected as suspected data points. Sensitivity analysis demonstrates that surface area, pore volume, pressure, and temperature have almost the same contribution on hydrogen storage. The findings of this research offer a valuable model for MOF selection and optimization of operational conditions in hydrogen storage processes that utilizes MOFs.