mlsanityData to 14 Oct 2026

Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

arXiv 2504.18880 ↗First posted 26 Apr 2025cs.AI cond-mat.mtrl-sci cs.CLcited by 2

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Abstract (arXiv)

Accurately identifying the synthesis conditions of metal-organic frameworks (MOFs) is essential for guiding experimental design, yet remains challenging because relevant information in the literature is often scattered, inconsistent, and difficult to interpret. We present MOFh6, a large language model driven system that reads raw articles or crystal codes and converts them into standardized synthesis tables. It links related descriptions across paragraphs, unifies ligand abbreviations with full names, and outputs structured parameters ready for use. MOFh6 achieved 99% extraction accuracy, resolved 94.1% of abbreviation cases across five major publishers, and maintained a precision of 0.93 +/- 0.01. Processing a full text takes 9.6 s, locating synthesis descriptions 36 s, with 100 papers processed for USD 4.24. By replacing static database lookups with real-time extraction, MOFh6 reshapes MOF synthesis research, accelerating the conversion of literature knowledge into practical synthesis protocols and enabling scalable, data-driven materials discovery.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 61 references that are in the map. First posted on arXiv 26 Apr 2025.

Builds on

6 of 61 references.

References · 6

  1. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  2. 2024Calibrating Large Language Models with Sample Consistencycited by 73
  3. 2023ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesiscited by 51
  4. 2024Integrating Chemistry Knowledge in Large Language Models via Prompt Engineeringcited by 10
  5. 2023A GPT-4 Reticular Chemist for Guiding MOF Discoverycited by 9
  6. 2024Automated Review Generation Method Based on Large Language Modelscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026LLM-driven design of physics-constrained constitutive models: two agents are better than onecited by 0
  2. 2026Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industrycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents
  2. 2026ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning
  3. 2026An LLM agent for end-to-end computational materials discovery
  4. 2025MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
  5. 2025L^2M^3OF: A Large Language Multimodal Model for Metal-Organic Frameworks
  6. 2025System of Agentic AI for the Discovery of Metal-Organic Frameworks
  7. 2026SimMOF: AI agent for Automated MOF Simulations
  8. 2025Mofasa: A Step Change in Metal-Organic Framework Generation