Agentic generation of verifiable rules for deterministic, self-expanding reaction classification

Multi-agent LLM pipeline auto-generates 14,073 chemistry reaction rules from patent corpus

Researchers present a fully automated pipeline using a multi-agent LLM framework to classify chemical reactions and generate verifiable reaction rules across 665,901 US patent reactions. The system expands a standard taxonomy from 68 to 14,073 classes without human curation, using a verification loop that tests each generated rule against the corpus. A lightweight fingerprint classifier trained on the output achieves 97.7% accuracy on unseen reactions, matching leading proprietary classifiers while extending to novel chemistries. The work demonstrates a general approach for converting generative LLMs into self-expanding symbolic rule systems.