Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis

Area of Science:

• Catalysis
• Artificial Intelligence
• Materials Science

Background:

• Heterogeneous catalysis research faces challenges with small, inconsistent datasets and non-standardized catalyst representations.
• Extracting fundamental knowledge requires integrating performance, characterization, and mechanistic data across scales.
• Current AI approaches struggle with the complexity and heterogeneity of catalysis data.

Purpose of the Study:

• To explore the potential of large language models (LLMs) in advancing heterogeneous catalysis research.
• To identify key opportunities for LLMs in standardizing data and extracting knowledge.
• To discuss the integration of LLMs with existing scientific validation methods.

Main Methods:

• Leveraging language as a unifying representation for diverse catalytic data modalities.
• Focusing on three core LLM applications: text-to-properties, text-to-structure, and text-to-mechanistic models.
• Examining LLM-readiness of data and alignment with scientific principles.

Main Results:

• LLMs can standardize dispersed experimental results, making them accessible for statistical modeling.
• Opportunities exist for LLMs to translate text into catalyst properties, structures, and mechanistic insights.
• Coupling LLM-generated hypotheses with physics-grounded validation yields verifiable and actionable representations.

Conclusions:

• Large language models offer a powerful approach to unify and analyze heterogeneous catalysis data.
• Standardizing data representation through LLMs enhances AI's role in catalyst discovery.
• Integrating LLMs with experimental validation bridges lab-scale findings to industrial applications.