LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation

Abstract

CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the catalyst discovery process by helping researchers explore chemical spaces and interpret results when augmented with retrieval-based grounding. We introduce a retrieval-augmented generation framework that enables GPT-4 to navigate chemical space by accessing a database of 50,000+ known materials, adapting general-purpose language understanding for high-throughput materials design. Our approach generated over 250 catalyst candidates with an 82% thermodynamic stability rate while addressing multi-objective constraints: 68% achieved <$100/kg cost with metallic conductivity (band gap<0.1eV) and mechanical stability (B/G>1.75). The best-performing Fe0.2Co0.2Ni0.2Ir0.1Ru0.3 achieves 0.285V limiting potential (25% improvement over IrO2), while Cr0.2Fe0.2Co0.3Ni0.2Mo0.1 optimally balances performance-cost trade-offs at $18/kg. Volcano plot analysis confirms that 78% of LLM-generated catalysts cluster near the theoretical activity optimum, while our system achieves 200x computational efficiency compared to traditional high-throughput screening. By demonstrating that retrieval-augmented generation can ground AI creativity in physical constraints without sacrificing exploration, this work demonstrates an approach where natural language interfaces can streamline materials discovery workflows, enabling researchers to explore chemical spaces more efficiently while the LLM assists in result interpretation and hypothesis generation.

Figures (10)

• Figure 1: LLM-driven catalyst discovery pipeline: RAG retrieval → LLM generation → DFT validation.
• Figure 2: Comprehensive comparison of material properties between known catalysts and LLM-generated catalysts (HEA: High-Entropy Alloy, DA: Doped Alloy). The visualization maps catalysts by mixing enthalpy and d-band center, with LLM-HEAs occupying the favorable lower-left quadrant. Property distributions show LLM-HEAs exhibit more negative mixing enthalpies (mean -0.794 eV/atom) indicating higher stability, and more negative d-band centers (mean -2.891 eV) correlating with enhanced catalytic activity.
• Figure 3: Volcano plot analysis showing the relationship between oxygen binding energy ($\Delta E_{*O}$) and theoretical overpotential for LLM-generated catalysts (blue circles) compared to known catalysts (red triangles). The optimal region near the volcano peak is highlighted, where most LLM candidates cluster, explaining their superior performance. Error bars represent standard deviations from ensemble DFT calculations.
• Figure 4: Performance ranking of all validated catalysts showing the distribution of limiting potentials. LLM-generated HEAs (blue) consistently outperform both traditional catalysts (red) and randomly generated compositions (gray). The top quartile is dominated by LLM discoveries, with 18 of the best 25 catalysts originating from our approach.
• Figure 5: Activity landscape and optimization paths showing the iterative refinement process. The contour map represents limiting potential as a function of $\Delta E_{NOH}$ and mixing enthalpy, with the best catalyst (red star) identified through systematic exploration. Red paths trace the convergence trajectory from initial candidates to the optimal composition, demonstrating efficient navigation of the 2D property space.