# ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

arXiv: https://arxiv.org/abs/2306.11296 · HTML: https://mlsanity.com/paper/2306.11296/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 62 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks](https://mlsanity.com/paper/2106.13327/) (2021)
- [A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models](https://mlsanity.com/paper/2210.14191/) (2022)

## Built on by (most cited first, 30 of 51)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Cross-Prediction-Powered Inference](https://mlsanity.com/paper/2309.16598/) (2023)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation](https://mlsanity.com/paper/2401.17244/) (2024)
- [CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning](https://mlsanity.com/paper/2503.13517/) (2025)
- [ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks](https://mlsanity.com/paper/2308.01423/) (2023)
- [Autonomous Agents for Collaborative Task under Information Asymmetry](https://mlsanity.com/paper/2406.14928/) (2024)
- [An Interdisciplinary Outlook on Large Language Models for Scientific Research](https://mlsanity.com/paper/2311.04929/) (2023)
- [Agent-based Learning of Materials Datasets from Scientific Literature](https://mlsanity.com/paper/2312.11690/) (2023)
- [From Generalist to Specialist: A Survey of Large Language Models for Chemistry](https://mlsanity.com/paper/2412.19994/) (2024)
- [VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities](https://mlsanity.com/paper/2412.18161/) (2024)
- [Image and Data Mining in Reticular Chemistry Using GPT-4V](https://mlsanity.com/paper/2312.05468/) (2023)
- [The Rise of Generative AI for Metal-Organic Framework Design and Synthesis](https://mlsanity.com/paper/2508.13197/) (2025)
- [Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie](https://mlsanity.com/paper/2601.12554/) (2026)
- [Future-proofing geotechnics workflows: accelerating problem-solving with large language models](https://mlsanity.com/paper/2312.12411/) (2023)
- [Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models](https://mlsanity.com/paper/2410.18136/) (2024)
- [Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks](https://mlsanity.com/paper/2505.08531/) (2025)
- [CataLM: Empowering Catalyst Design Through Large Language Models](https://mlsanity.com/paper/2405.17440/) (2024)
- [LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations](https://mlsanity.com/paper/2408.04665/) (2024)
- [Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design](https://mlsanity.com/paper/2408.11793/) (2024)
- [Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System](https://mlsanity.com/paper/2505.20310/) (2025)
- [MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature](https://mlsanity.com/paper/2509.01042/) (2025)
- [Automated Extraction of Material Properties using LLM-based AI Agents](https://mlsanity.com/paper/2510.01235/) (2025)
- [Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo](https://mlsanity.com/paper/2405.02128/) (2024)
- [Reshaping MOFs text mining with a dynamic multi-agents framework of large language model](https://mlsanity.com/paper/2504.18880/) (2025)
- [Doc2SAR: A Synergistic Framework for High-Fidelity Extraction of Structure-Activity Relationships from Scientific Documents](https://mlsanity.com/paper/2506.21625/) (2025)
- [How False Data Affects Machine Learning Models in Electrochemistry?](https://mlsanity.com/paper/2311.10795/) (2023)
- [Pathway to a fully data-driven geotechnics: lessons from materials informatics](https://mlsanity.com/paper/2312.00581/) (2023)
- [Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property Prediction](https://mlsanity.com/paper/2405.10448/) (2024)
- [Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?](https://mlsanity.com/paper/2407.08922/) (2024)

## Abstract (arXiv)

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines.