CatAgent: Multi-Agent Orchestration for Electrocatalyst Discovery

The discovery of efficient electrocatalysts is hindered by the combinatorial scale of candidate material space. Here we present CatAgent, an autonomous multi-agent workflow driven by large language models that achieves up to a 9.65-fold increase in discovery rates over adsorbate-specific random baselines. We benchmark 13 language models in single-shot and iterative modes across bimetallic alloy compositions. Critic-enabled iterations improve performance for most architectures, with top models concentrating proposals near zero theoretical overpotential. Our results suggest that catalyst screening can benefit from LLM-guided chemical reasoning.