\section{Discussion and Conclusion}
\label{sec:discussion}

We demonstrated that RAG-enhanced LLMs can accelerate catalyst discovery, achieving 82\% stability and 25\% performance improvement over baselines. The best catalyst, Fe$_{0.2}$Co$_{0.2}$Ni$_{0.2}$Ir$_{0.1}$Ru$_{0.3}$, reached 0.285V limiting potential—substantially exceeding our 15-20\% improvement target. This success stems from combining the model's implicit knowledge with 50,000+ retrieved examples, enabling efficient navigation of $10^8$-dimensional HEA space.

\textbf{Key achievements:} (1) 3.6× stability improvement with RAG (82\% vs 23\% without); (2) 78\% of catalysts near volcano optimum; (3) 200× computational efficiency (4,200 vs 840,000 CPU-hours); (4) 68\% achieved favorable multi-objective trade-offs (<\$100/kg, metallic conductivity, B/G>1.75). Discovery of Fe-Co synergy (15\% above linear mixing) and 30\% novel structural motifs demonstrates the model's capacity to identify non-obvious patterns beyond traditional screening.

\textbf{Limitations:} While we incorporated conductivity, mechanical stability, and cost constraints, full Pareto optimization remains computationally prohibitive. DFT calculations assume ideal surfaces (10-15\% uncertainty) and cannot capture degradation kinetics. Some promising compositions require >2000°C processing, limiting practical feasibility. Extended analysis in Appendix G.

\textbf{Broader impact:} The RAG-LLM paradigm extends beyond catalysts to battery electrodes and quantum materials without specialized training. By eliminating fine-tuning requirements, this approach democratizes AI-assisted discovery for resource-constrained researchers. Integration with automated synthesis platforms could enable closed-loop discovery systems, while extracting the LLM's learned design principles could advance fundamental materials understanding.

Our work establishes that properly grounded general-purpose AI serves as a powerful research assistant, amplifying human expertise to accelerate materials innovation critical for climate solutions. The journey from skepticism about LLMs in chemistry to validated discoveries proves that effective human-AI collaboration can transcend traditional domain boundaries, opening new frontiers in scientific discovery.