Reasoning Language Model as Rule Finder: A Case Study on C-H Bond Activation Using 2D Metal-Organic Frameworks.

Unraveling the structure-activity relationship in catalysis requires interpretable models that can extract governing principles from complex data sets. This study explores reasoning large language models (LLMs) as rule-finders for predicting C-(sp)-H activation outcomes catalyzed by 2D Fe-terpyridine MOFs. Surface modifications with molecular modifiers systematically modulate the catalytic microenvironment, but linking modifier structure to activity remains challenging. While traditional descriptors offer high predictive accuracy, LLM-derived rules provide interpretable insights. Integrating LLM reasoning with experimental features (e.g., Fe-loading, modifier ratios) identified para-substituted benzoates with electron-withdrawing or coordinating groups as performance boosters. Validated by machine learning, this rule achieved 82.6% prediction accuracy. Notably, the coordinating group can become electron-withdrawing upon Fe coordination or protonation. The LLM revealed that modifiers tune the catalyst's electronic state rather than directly interacting with intermediates/transition states, bridging data-driven predictions with mechanistic understanding. This highlights LLM's potential to derive chemically meaningful rules in catalysis.