Graph‐Based Deep Learning for Optimized Design of <scp>HSV</scp> Using Advanced Composite Materials

ABSTRACT Hydrogen storage vessels (HSVs) demand sophisticated composites with excellent strength‐to‐weight values to withstand extreme pressure while maintaining safety and durability. Accurate prediction of the mechanical properties of these composites is critical for optimizing their performance. However, traditional empirical models, which involve complex, non‐linear interactions, are frequently difficult for computers to portray between various composite constituents. To overcome this limitation, this study introduces a Sparse Spectral Graph Convolutional Network (SGCN)‐based framework for predicting the mechanical behavior of material composites for purposes involving hydrogen storage. The approach leverages graph‐based deep learning to model the relationships among key material features such as matrix type, reinforcement type, fiber orientation, and mechanical strength. By encoding these properties into a structured graph, the SGCN effectively learns spatial and relational dependencies within the data. The system is developed in Python and validated using an open‐source dataset of composite materials. Comparative experiments with Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN) are examples of conventional machine learning models that demonstrate that the SGCN achieves superior accuracy in capturing complex material characteristics. This research presents a scalable, data‐driven solution for enhancing the creation and improvement of composite hydrogen storage containers.