Abstract
We introduce StableOx-Cat, an artificial intelligence (AI)-agent framework that enables systematic and reliable exploration of stable metal oxide (MO) electrocatalysts via a unified natural-language interface. StableOx-Cat integrates a large language model (LLM) for intent understanding and task orchestration with deterministic, physics-based analysis tools for electrocatalysis evaluation. User queries expressed in natural language are automatically parsed into structured actions, including database statistics, bulk thermodynamic stability screening based on energy-above-hull criteria, and aqueous electrochemical stability analysis under user-defined pH values and electrochemical potential windows. By applying the physical criteria to screen the stable MO electrocatalysts, StableOx-Cat avoids hallucinations and ensures a physically based stability analysis. This Agent enables the assessment of aqueous electrochemical stability across a wide range of reactions, with applied potentials spanning -2 to 2 V versus standard hydrogen electrode and pH values ranging from 0 to 14. Representative use cases demonstrate how StableOx-Cat enables flexible stability screening of MOs under both thermodynamic and aqueous environments. In addition, the agent architecture supports integration with different LLMs for task execution and query parsing. Overall, StableOx-Cat provides an accessible platform for stability-oriented materials exploration, offering a practical pathway to accelerate the discovery of experimentally relevant MO electrocatalysts for electrochemical applications, and can be generalized to other classes of electrocatalysts, such as alloys, metal nitrides, and carbides.