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[2602.21533] Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Summary
This paper presents the MAESTRO framework, which utilizes multi-agent large language models to discover high-performance single atom catalysts for the oxygen reduction reaction, showcasing a novel approach in materials science.
Why It Matters
The research highlights the transformative potential of large language models in materials discovery, particularly in catalysis. By employing a reasoning-driven design, this framework could lead to breakthroughs in energy conversion technologies, addressing critical challenges in sustainability and efficiency.
Key Takeaways
- MAESTRO framework employs multiple LLMs for collaborative catalyst discovery.
- The approach leverages reasoning and in-context learning to identify new design principles.
- Results demonstrate the ability to break conventional scaling relations in catalysis.
- This method could significantly enhance the efficiency of materials discovery.
- The findings suggest a paradigm shift in how complex scientific tasks are approached.
Condensed Matter > Materials Science arXiv:2602.21533 (cond-mat) [Submitted on 25 Feb 2026] Title:Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework Authors:Dong Hyeon Mok, Seoin Back, Victor Fung, Guoxiang Hu View a PDF of the paper titled Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework, by Dong Hyeon Mok and 3 other authors View PDF Abstract:Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally require human expertise. This progress has extended into materials discovery, where LLMs introduce a new paradigm by leveraging reasoning and in-context learning, capabilities absent from conventional machine learning approaches. Here, we present a Multi-Agent-based Electrocatalyst Search Through Reasoning and Optimization (MAESTRO) framework in which multiple LLMs with specialized roles collaboratively discover high-performance single atom catalysts for the oxygen reduction reaction. Within an autonomous design loop, agents iteratively reason, propose modifications, reflect on results and accumulate design history. Through in-context learning enabled by this iterative process, MAESTRO identified design principles not explicitly encoded in the LLMs' background knowledge and successfully discovered catalysts that break conventional scaling relations between react...
