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

We demonstrated that LLMs without chemistry-specific fine-tuning can discover high-performance catalysts via retrieval-augmented generation. Our approach achieved 82\% stability rate and 25\% improved limiting potentials versus baselines. Fe$_{0.2}$Co$_{0.2}$Ni$_{0.2}$Ir$_{0.1}$Ru$_{0.3}$ achieved 0.285V, validating that properly grounded general-purpose AI tackles specialized scientific challenges.

RAG transformed LLMs into materials designers, achieving 3.6× better stability than unaugmented generation. 78\% of catalysts clustered near optimal binding energies on volcano plots, demonstrating implicit catalysis understanding. The 200× computational efficiency enables previously intractable searches.

By eliminating specialized training requirements, we democratize AI-assisted materials design through natural language interfaces. Discovery of novel motifs suggests LLMs contribute conceptual innovation beyond pattern matching. Current limitations—computational validation, single-objective optimization—define development paths rather than fundamental obstacles.

Future directions include automated synthesis integration, multi-objective optimization, and extension to batteries, photovoltaics, quantum materials. Explainable AI could extract LLM-learned design principles, advancing fundamental materials understanding.

We present democratized scientific discovery where AI amplifies human creativity. Climate challenges demand accelerating discovery from decades to months—now achievable without massive resources or specialized expertise. RAG bridges general AI with specialized knowledge, enabling breakthrough discoveries through human-machine synergy. This validates that properly grounded general intelligence transcends domain boundaries, revolutionizing scientific discovery approaches.