Uganda

The urgency for sustainable and efficient hydrogen production has increased interest in heterostructured nanomaterials known for their excellent photocatalytic properties. Traditional synthesis methods often rely on trial-and-error resulting in inefficiencies in material discovery and optimization. This work presents a new AI-driven framework that overcomes these challenges by integrating advanced machine-learning techniques specific to heterostructured nanomaterials. Graph Neural Networks (GNNs) enable accurate representations of atomic structures predicting material properties like bandgap energy and photocatalytic efficiency within ±0.05 eV. Reinforcement Learning optimises synthesis parameters reducing experimental iterations by 40% and boosting hydrogen yield by 15–20%. Physics-Informed Neural Networks (PINNs) successfully predict reaction pathways and intermediate states minimizing synthesis errors by 25%. Variational Autoencoders (VAEs) generate novel material configurations improving photocatalytic efficiency by up to 15%. Additionally Bayesian Optimisation enhances predictive accuracy by 30% through efficient hyperparameter tuning. This holistic framework integrates material design synthesis optimization and experimental validation fostering a synergistic data flow. Ultimately it accelerates the discovery of novel heterostructured nanomaterials enhancing efficiency scalability and yield thus moving closer to sustainable hydrogen production with improvements in photolytic efficiency setting a benchmark for AI-assisted research.