Text mining in MOF research: from manual curation to large language model-based automation

Area of Science:

• Materials Science
• Computational Chemistry
• Data Science

Background:

• The vast and rapidly growing body of metal-organic framework (MOF) literature poses challenges for efficient knowledge extraction.
• Text mining offers a solution by transforming unstructured scientific text into structured, usable data for MOF research.

Purpose of the Study:

• To review the evolution of text mining techniques applied to MOF research.
• To analyze the impact of natural language processing (NLP), machine learning (ML), and large language models (LLMs) on MOF data extraction.
• To explore the applications and future potential of text mining in the MOF domain.

Main Methods:

• Review of historical text mining approaches (manual curation, rule-based methods).
• Analysis of foundational NLP and ML techniques (named entity recognition, vector embeddings).
• In-depth examination of LLM-based frameworks for information extraction and their accuracy.

Main Results:

• LLM-based automation represents a significant breakthrough in MOF text mining.
• Text mining methods, particularly LLMs, demonstrate accuracy in predicting MOF synthesizability, properties, and stability.
• Various applications of text mining in MOF research are identified and compared.

Conclusions:

• Text mining, especially LLM-driven approaches, is crucial for accelerating data-driven MOF research.
• Future directions include integrating text mining into interactive UIs, autonomous labs, and multi-modal AI systems.
• This review provides a foundation for researchers to adopt and advance text mining in MOF science.