Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence (TRACE-AI): Community Guidelines and A Publication Checklist

Artificial intelligence (AI) is increasingly integrated into catalysis science, enabling agentic workflows in which AI systems perceive inputs, reason under constraints, plan, and autonomously execute in silico or physical experiments with minimal human intervention. While these closed-loop capabilities hold promise to accelerate knowledge generation and technological innovation, they inevitably introduce new sources of variability in data lineage, model specification, and agent policies that can undermine FAIRness, rigor, and reproducibility. These risks are particularly pronounced in heterogeneous catalysis, where subtleties in catalyst synthesis and pretreatment, dynamic restructuring under operating conditions, and transport-mediated local environments can largely determine catalytic outcomes. To address these challenges, we introduce TRACE-AI (Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence) as a set of community guidelines paired with a publication checklist. TRACE-AI emphasizes end-to-end traceability across the full lifecycle of an agentic catalysis campaign, linking research objectives to data and models, agent reasoning and action, and the knowledge acquired. By promoting standardized and accountable reporting, TRACE-AI aims to cultivate a shared foundation for accelerating scientific discovery while reinforcing safety and trust as autonomous catalysis laboratories continue to emerge.