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ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

Author

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  • Yeonghun Kang

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Jihan Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. ChatMOF shows high accuracy rates of 96.9% for searching, 95.7% for predicting, and 87.5% for generating tasks with GPT-4. Additionally, it successfully creates materials with user-desired properties from natural language. The study further explores the merits and constraints of utilizing large language models (LLMs) in combination with database and machine learning in material sciences and showcases its transformative potential for future advancements.

Suggested Citation

  • Yeonghun Kang & Jihan Kim, 2024. "ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48998-4
    DOI: 10.1038/s41467-024-48998-4
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    References listed on IDEAS

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    1. Seyed Mohamad Moosavi & Aditya Nandy & Kevin Maik Jablonka & Daniele Ongari & Jon Paul Janet & Peter G. Boyd & Yongjin Lee & Berend Smit & Heather J. Kulik, 2020. "Understanding the diversity of the metal-organic framework ecosystem," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Maciej P. Polak & Dane Morgan, 2024. "Extracting accurate materials data from research papers with conversational language models and prompt engineering," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
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