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

Our results—82\% stability, 25\% performance improvement, 78\% near volcano optimum—demonstrate that general-purpose LLMs can significantly enhance materials discovery workflows when properly grounded through RAG. This represents a paradigm shift in how researchers can leverage AI to accelerate exploration and interpretation of complex chemical spaces.

\textbf{Why RAG succeeded:} Ablations showed 3.6× stability improvement with RAG (82\% vs 23\%), transforming LLMs from pattern generators to sophisticated research assistants capable of navigating chemical constraints. The model's implicit knowledge combined with 50,000+ retrieved examples enabled efficient navigation of $10^8$-dimensional HEA space. Discovery of Fe-Co synergy (15\% above linear mixing) exemplifies pattern recognition beyond traditional screening.

\textbf{Advantages:} (1) 200× computational efficiency (4,200 vs 840,000 CPU-hours), scaling to 300,000× for 6-element HEAs; (2) No training required unlike ML models needing months of data collection; (3) 75\% of LLM-HEAs achieved $\eta<0.40V$ vs 12\% known catalysts; (4) Natural language interface enhances research workflows, enabling more efficient exploration while researchers maintain control over interpretation and validation.

\textbf{Limitations and Multi-Objective Considerations:} (1) While we incorporated conductivity screening (band gap analysis), mechanical stability (Pugh's ratio), and cost evaluation (\$/kg calculations), full Pareto optimization across all objectives remains computationally prohibitive under experimental constraints. Our approach represents a pragmatic compromise: we applied multi-objective constraints during generation and post-hoc filtering rather than true simultaneous optimization. Table~\ref{tab:top_catalysts} demonstrates that 68\% of catalysts achieved favorable trade-offs (<\$100/kg, metallic conductivity, B/G>1.75), though experimental validation of these predicted properties remains essential. (2) DFT calculations assume ideal surfaces (10-15\% uncertainty) and cannot capture degradation kinetics or long-term stability under operational conditions—critical factors that only electrochemical testing can verify. (3) Mechanical properties estimated via elastic constants may not reflect synthesis challenges; some promising compositions require >2000°C processing temperatures, limiting practical feasibility. (4) Inherent biases from LLM training and database composition (predominantly transition metals) may overlook unconventional but effective compositions. Despite these limitations, our multi-constraint approach demonstrates that LLMs can navigate complex trade-offs when properly guided, achieving reasonable balance across competing objectives within computational boundaries. Extended limitations in Appendix G.

\textbf{Implications:} RAG-LLM paradigm extends beyond catalysts to battery electrodes, quantum materials without specialized models. Discovery of 30\% novel motifs suggests the system's capacity to augment human creativity in identifying non-obvious patterns. \textbf{Future directions:} (1) Multi-objective optimization incorporating conductivity/stability/cost; (2) Synthesis-aware retrieval strategies; (3) Active learning integration; (4) Automated experimental validation loops; (5) Extraction of implicit design principles. Enhanced discovery workflows enable researchers worldwide to accelerate materials innovation for climate solutions through more efficient human-AI collaboration.