Efficient hydrogen (H \(_2\) ) storage remains a major challenge for clean energy applications. This study presents an AI-driven methodology to optimize H \(_2\) storage in porous carbon adsorbents. A comprehensive dataset of 917 literature-derived entries was used to develop two machine learning models: Random Forest (RF) and Convolutional Neural Network (CNN). Both models accurately predicted hydrogen uptake based on material properties and experimental conditions. Within the range of the experimental dataset, the CNN demonstrated strong interpolation performance, accurately predicting hydrogen uptake with a high coefficient of determination ( \(R^2\) = 0.9353) and a Root Mean Squared Error (RMSE) of 0.0406. The CNN was integrated into a multi-objective optimization framework to maximize hydrogen uptake while minimizing average pore diameter (AVD). Through extrapolative optimization beyond the training data range, the AI-driven technique and optimization method (AiDO) identified theoretical Pareto-optimal solutions extending beyond the experimental dataset, predicting H \(_2\) uptake of up to 16.66 wt% at an AVD of 0.08 nm. While these extrapolated solutions are not directly validated by experiments, constrained optimization scenarios (e.g., realistic pore-size limits) provide physically meaningful design targets. Sensitivity analysis confirmed the robustness of the methodology to different normalization techniques. This approach demonstrates the potential of combining predictive ML with optimization to accelerate the design of high-performance hydrogen adsorbents, reducing experimental costs and supporting sustainable energy systems.
