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

AI Scientists; Xinyi Lin; Danqing Yin; Ying Guo

cond-mat.mtrl-sciMar 16, 2026

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

Authors: AI Scientists, Xinyi Lin, Danqing Yin, Ying Guo

Organizations: School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, 999077, China · Laboratory of Data Discovery for Health Limited (D24H), Pak Shek Kok, Hong Kong SAR, 999077, China · Room 312, Lau Chung Him Building, 8 Castle Peak Road, Tuen Mun, Hong Kong

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 & tables

Figure 1: LLM-driven catalyst discovery pipeline: RAG retrieval → LLM generation → DFT validation.

Catalyst Composition	$\eta_{OER}$	$E_{hull}$	Band Gap	B/G	Cost	Score ∗
	(V)	(meV/atom)	(eV)	Ratio	($/kg)
Fe 0.2 Co 0.2 Ni 0.2 Ir 0.1 Ru 0.3	0.285	32	0.0	2.1	27,000	0.72
Mn 0.15 Fe 0.25 Co 0.25 Ni 0.2 Pt 0.15	0.298	28	0.0	1.9	4,500	0.85
Cr 0.2 Fe 0.2 Co 0.3 Ni 0.2 Mo 0.1	0.312	41	0.0	2.3	18	0.91
V 0.1 Cr 0.2 Mn 0.2 Fe 0.25 Co 0.25	0.325	37	0.08	1.8	15	0.88
Ti 0.1 Fe 0.3 Co 0.3 Ni 0.2 Cu 0.1	0.334	45	0.0	2.0	19	0.89

Table 1: Multi-objective performance comparison of top LLM-generated catalysts. Beyond catalytic activity (

\eta_{OER}

), we evaluate conductivity (band gap), mechanical stability (Pugh’s ratio B/G), and material cost. Statistical significance assessed using Wilcoxon signed-rank test with Bonferroni correction (

\alpha

=0.0002 for 250 comparisons).

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.

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Detailed ablation results showing RAG impact on thermodynamic stability (3.6× improvement), comparison of different prompt engineering strategies, and iterative refinement convergence over 5 cycles demonstrating plateau at cycle 4.

Configuration	Stability (%)	$\eta_{OER}$ (V)	Diversity	Time (h)
Full System	$82.4\pm 1.8$	$0.362\pm 0.015$	3.2	24
No RAG	$23.1\pm 4.2$	$0.521\pm 0.043$	4.1	18
No Iteration	$64.3\pm 3.1$	$0.412\pm 0.021$	3.0	5
Constraint Only	$68.2\pm 2.7$	$0.395\pm 0.018$	1.8	22
Analogy Only	$41.3\pm 3.9$	$0.438\pm 0.027$	3.5	21
Random Baseline	$3.2\pm 1.1$	$0.612\pm 0.071$	4.5	20

Appendix

Table 2: Full ablation study examining all component combinations. Each configuration tested with 200 generated candidates over 5 independent runs.

Parameter	Range Tested	Optimal	Impact
Temperature	0.1-1.0	0.7	Critical
Top-p	0.5-1.0	0.95	Moderate
k (retrieval)	5-50	20	High
Similarity threshold	0.7-0.95	0.85	Low
Beam width	1-10	5	Moderate
Iterations	1-10	5	High

Appendix

Table 3: Extended hyperparameter sensitivity analysis

Comparison	Cohen’s d	Interpretation
LLM vs IrO 2 baseline	2.31	Very large
LLM vs known catalysts	1.87	Large
With RAG vs without	3.42	Very large
Combined vs constraint-only prompts	1.42	Large
Combined vs analogy-only prompts	2.18	Very large

Appendix

Table 10

Source	Entries	Material Types	Avg. Elements
Materials Project	25,000	Bulk alloys, oxides	3.8
NOMAD	12,000	Alloys, surfaces	4.2
OC20	13,000	Surface+adsorbates	2.5
Total	50,000	—	3.6

Appendix

Table 4: Distribution of database entries across sources and material types

Figure 7: Complete correlation matrix showing relationships between all catalyst properties including overpotential, stability metrics, d-band center, and compositional features for the full set of LLM-generated catalysts.

Figure 8: 3D activity landscape of HEA catalysts showing the relationship between NOH adsorption energy ( $\Delta E_{NOH}$ ), mixing enthalpy, and limiting potential. The surface color represents catalytic activity, with dark purple regions indicating optimal performance. Black circles mark individual catalyst compositions, demonstrating clustering in the favorable low-potential region.

Figure 9: Statistical comparison of key properties across catalyst types. Box plots show mixing enthalpy distribution with LLM-HEAs exhibiting most negative values (median -0.8 eV/atom) indicating superior stability, and d-band center distribution with LLM-HEAs centered at -2.8 eV correlating with enhanced activity.

Figure 10: Property distributions for HEA catalysts showing mixing enthalpy right-skewed distribution (mean -0.593 eV/atom), multimodal d-band center distribution (mean -2.425 eV), broad $\Delta E_{NOH}$ distribution (mean 0.774 eV), and left-skewed limiting potential distribution with exceptional catalysts in the tail. Vertical lines indicate mean (red) and median (green) values.

Feature	$\eta_{OER}$	Stability	d-band	EN	Size
$\eta_{OER}$	1.00
Stability	-0.42**	1.00
d-band center	-0.73***	0.31*	1.00
Avg. EN	0.28*	-0.19	-0.35**	1.00
Size mismatch	0.15	-0.52***	-0.08	0.21	1.00
Fe content	-0.38**	0.27*	0.41**	-0.15	-0.03

Appendix

Table 5: Pearson correlations. *p<0.05, **p<0.01, ***p<0.001 after Bonferroni correction

Composition	Method	Conditions
Fe 0.2 Co 0.2 Ni 0.2 Ir 0.1 Ru 0.3	Arc melting	1800 ∘ C, Ar
Mn 0.15 Fe 0.25 Co 0.25 Ni 0.2 Pt 0.15	Sputtering	400 ∘ C, 5 mTorr
Cr 0.2 Fe 0.2 Co 0.3 Ni 0.2 Mo 0.1	Ball milling	500 rpm, 20h
V 0.1 Cr 0.2 Mn 0.2 Fe 0.25 Co 0.25	Carbothermal	2000 ∘ C flash

Appendix

Table 17

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