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

arXiv: https://arxiv.org/abs/2504.18880 · HTML: https://mlsanity.com/paper/2504.18880/

## 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 (most cited first, 6 of 6)

- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Calibrating Large Language Models with Sample Consistency](https://mlsanity.com/paper/2402.13904/) (2024)
- [ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis](https://mlsanity.com/paper/2306.11296/) (2023)
- [Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering](https://mlsanity.com/paper/2404.14467/) (2024)
- [A GPT-4 Reticular Chemist for Guiding MOF Discovery](https://mlsanity.com/paper/2306.14915/) (2023)
- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)

## Built on by (most cited first, 2 of 2)

- [LLM-driven design of physics-constrained constitutive models: two agents are better than one](https://mlsanity.com/paper/2605.23754/) (2026)
- [Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry](https://mlsanity.com/paper/2605.27071/) (2026)

## 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.