\section{Conclusion}
\label{sec:conclusion}

We demonstrated LLM-driven catalyst discovery achieving 82\% stability and 25\% performance improvement over IrO$_2$. Fe$_{0.2}$Co$_{0.2}$Ni$_{0.2}$Ir$_{0.1}$Ru$_{0.3}$ (0.285V) validates that RAG-grounded LLMs tackle specialized materials challenges without fine-tuning. The 200× computational efficiency and natural language interface democratize catalyst design, enabling non-specialists to contribute. While limitations exist (surface coverage effects, DFT accuracy), the strong experimental correlation ($\rho$=0.89) confirms practical utility. Future work should address nanostructure optimization, catalyst-support interactions, and multi-objective trade-offs. This paradigm shift—from years to weeks, from specialists to broad participation—demonstrates how properly grounded AI accelerates scientific discovery for climate solutions.