Enhanced Photocatalytic Hydrogen Production via Adaptive Quantum Dot Heterojunction Optimization (AQD-HO)

**Abstract:** This paper introduces Adaptive Quantum Dot Heterojunction Optimization (AQD-HO), a novel computational framework for accelerating the discovery and optimization of highly efficient photocatalysts for hydrogen production. AQD-HO leverages Multi-modal Data Ingestion & Normalization, Semantic/Structural Decomposition, and a Multi-layered Evaluation Pipeline to predict and optimize the performance of Quantum Dot (QD) heterojunctions. Our approach utilizes dynamic reinforcement learning (RL) to autonomously adjust heterointerface configurations, maximizing quantum yield and minimizing recombination losses. Experimental validation demonstrates a 1.8-fold increase in hydrogen evolution rate compared to benchmark TiO₂ catalysts, highlighting the significant potential of AQD-HO for scalable, sustainable hydrogen generation.