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
Organizations: Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul 04107, Republic of Korea · KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Republic of Korea · Department of Integrative Energy Engineering, Korea University, Seoul 02841, Republic of Korea · Institute for Multiscale Matter and Systems (IMMS), Ewha Womans University, Seoul 03760, Republic of Korea · School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. · School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. · School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30332, USA.
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 reaction intermediates. These results highlight the potential of multi-agent LLM frameworks as a powerful strategy to generate chemical insight and discover promising catalysts.
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
Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Department of Chemical and Biomolecular Engineering, University of Nebraska–Lincoln, Lincoln, NE 68588, USA · Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA +3
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea +5