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

Area of Science:

• Catalysis
• Materials Science
• Computational Chemistry

Background:

• Understanding structure-activity relationships (SAR) in catalysis is crucial for designing efficient catalysts.
• Predicting outcomes of C-(sp3)-H activation catalyzed by 2D Fe-terpyridine metal-organic frameworks (MOFs) is challenging due to complex data sets.
• Surface modifications with molecular modifiers systematically alter the catalytic microenvironment, complicating SAR analysis.

Purpose of the Study:

• To explore the use of reasoning large language models (LLMs) as rule-finders for predicting C-(sp3)-H activation outcomes.
• To identify governing principles linking molecular modifier structure to catalytic activity in 2D Fe-terpyridine MOFs.
• To bridge data-driven predictions with mechanistic understanding in catalysis.

Main Methods:

• Utilized reasoning large language models (LLMs) to analyze catalytic data.
• Integrated LLM reasoning with experimental features like Fe-loading and modifier ratios.
• Employed machine learning for validation of LLM-derived rules.

Main Results:

• LLM-derived rules provided interpretable insights into catalytic activity, complementing traditional descriptors.
• Identified para-substituted benzoates with electron-withdrawing or coordinating groups as performance-enhancing modifiers.
• Achieved 82.6% prediction accuracy for catalytic activity using the validated LLM-derived rule.
• Revealed that modifiers tune the catalyst's electronic state rather than directly interacting with intermediates.

Conclusions:

• LLMs can effectively derive chemically meaningful rules for catalysis.
• The study demonstrates LLM's potential for uncovering SAR in heterogeneous catalysis.
• LLM-based rule discovery offers a pathway to enhanced mechanistic understanding in catalyst design.