Work

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

Year: 2025

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide 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 3 )–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 3+ 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.

Source: ACS Central Science

DOI: 10.1021/acscentsci.5c00561