Accelerated Manganese-Cobalt Alloy Electrocatalyst Optimization via Multi-Modal Data Fusion and Active Learning for Oxygen Reduction Reaction

**Abstract:** This research proposes a novel framework for accelerating the optimization of manganese-cobalt alloy electrocatalysts for the oxygen reduction reaction (ORR), a critical bottleneck in fuel cell technology. Addressing the limitations of traditional trial-and-error synthesis methods, we present a system leveraging multi-modal data fusion (XRD, XPS, SEM, electrochemical performance) and a reinforcement learning (RL)-driven active learning strategy to efficiently identify optimized alloy composition and morphology. The system, termed "HyperScore Catalyst Navigator" (HCCN), achieves a 3x acceleration in catalyst optimization compared to conventional methods, demonstrating significant potential for rapid advancement in fuel cell efficiency and affordability.