Graph-Based Machine Learning Framework for Predicting Hydrogen Storage Capacity in Metal-Organic Frameworks.

Hydrogen is a clean and high-energy fuel, yet its safe and efficient storage remains a key obstacle to widespread adoption. Metal-organic frameworks (MOFs), with their high surface area and tunable porosity, have emerged as promising candidates for solid-state hydrogen storage. In this work, we introduce a graph-based machine learning framework for predicting hydrogen uptake in MOFs by integrating spectral graph theory with data-driven modeling. Molecular structures are represented as weighted graphs from which we extract 20 graph-based descriptors─including Laplacian spectral features, degree statistics, and Zagreb indices─that capture both topological and geometric characteristics of the framework. These interpretable descriptors are used to train multiple regression models on a data set of 3300 MOFs from the Cambridge Structural Database. The XGBoost regressor achieved the highest performance in predicting hydrogen uptake, with a coefficient of determination (R2) of 0.737, RMSE of 0.850% wt, and MAE of 0.433% wt for gravimetric uptake (UG); and a coefficient of determination (R2) of 0.698, RMSE of 4.467 g H2/L, and MAE of 3.045 g H2/L for volumetric uptake (UV). Beyond accurate prediction, the framework enables inverse materials design by identifying graph-based motifs that contribute to improved storage capacity. This integration of chemical graph theory with machine learning provides a scalable, interpretable, and computationally efficient pathway for the discovery of next-generation MOFs tailored for hydrogen storage and other clean energy applications.