AgentCAT Extracts and Analyzes Catalytic Reaction Data
This arXiv paper (submitted Feb 10, 2026) presents AgentCAT, an LLM agent that extracts and analyzes catalytic reaction data from chemical engineering publications. It introduces a schema-governed extraction pipeline, a dependency-aware reaction-network knowledge graph, and natural-language querying and visualization, evaluated on roughly 800 peer-reviewed papers. The system aims to alleviate data bottlenecks and enable cross-paper mechanistic and outcome analysis for catalysis researchers.
Key Points
- 1Introduces AgentCAT, an LLM agent extracting catalytic reaction data from ~800 chemical engineering papers
- 2Presents schema-governed extraction, dependency-aware reaction-network knowledge graph, and natural-language querying for traceability
- 3Enables cross-paper analysis and visualization, reducing data bottleneck for catalytic process research
Scoring Rationale
Relevant and actionable LLM extraction work across 800 papers, limited by preprint status and domain-specific scope.
Sources
Public references used for this report.
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