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  • Perspective
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Large language models for reticular chemistry

Nature Reviews Materials volume 10, pages 369–381 (2025) Cite this article

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Abstract

Reticular chemistry is the science of connecting molecular building units into crystalline extended structures such as metal–organic frameworks and covalent organic frameworks. Large language models (LLMs), a type of generative artificial intelligence system, can augment laboratory research in reticular chemistry by helping scientists to extract knowledge from literature, design materials and collect and interpret experimental data — ultimately accelerating scientific discovery. In this Perspective, we explore the concepts and methods used to apply LLMs in research, including prompt engineering, knowledge and tool augmentation and fine-tuning. We discuss how ‘chemistry-aware’ models can be tailored to specific tasks and integrated into existing practices of reticular chemistry, transforming the traditional ‘make, characterize, use’ protocol driven by empirical knowledge into a discovery cycle based on finding synthesis–structure–property–performance relationships. Furthermore, we explore how modular LLM agents can be integrated into multi-agent laboratory systems, such as self-driving robotic laboratories, to streamline labour-intensive tasks and collaborate with chemists and how LLMs can lower the barriers to applying generative artificial intelligence and data-driven workflows to such challenging research questions as crystallization. This contribution equips both computational and experimental chemists with the insights necessary to harness LLMs for materials discovery in reticular chemistry and, more broadly, materials science.

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Fig. 1: Progress in reticular chemistry over the past three decades.
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Fig. 2: Overview of key concepts in leveraging large language models for reticular chemistry.
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Fig. 3: Overview of key steps in data mining from scientific literature.
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Fig. 4: Examples of MOF synthesis parameters extracted from literature.
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Fig. 5: Generating molecular building block structures using fine-tuned large language models.
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Fig. 6: The roles LLMs can take in a data-driven selection process for reticular frameworks.
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Acknowledgements

The authors thank the Defense Advanced Research Projects Agency (DARPA) for the financial support under contract HR0011-21-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA.

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Authors and Affiliations

  1. Department of Chemistry, University of California — Berkeley, Berkeley, CA, USA

    Zhiling Zheng  (郑志凌), Nakul Rampal, Theo Jaffrelot Inizan & Omar M. Yaghi

  2. Bakar Institute of Digital Materials for the Planet, Berkeley, CA, USA

    Zhiling Zheng  (郑志凌), Nakul Rampal, Theo Jaffrelot Inizan, Christian Borgs, Jennifer T. Chayes & Omar M. Yaghi

  3. Department of Electrical Engineering and Computer Sciences, University of California — Berkeley, Berkeley, CA, USA

    Zhiling Zheng  (郑志凌), Nakul Rampal, Theo Jaffrelot Inizan, Christian Borgs & Jennifer T. Chayes

  4. Department of Mathematics, University of California — Berkeley, Berkeley, CA, USA

    Jennifer T. Chayes

  5. Department of Statistics, University of California — Berkeley, Berkeley, CA, USA

    Jennifer T. Chayes

  6. School of Information, University of California — Berkeley, Berkeley, CA, USA

    Jennifer T. Chayes

  7. KACST–UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia

    Omar M. Yaghi

Authors
  1. Zhiling Zheng  (郑志凌)
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  2. Nakul Rampal
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  3. Theo Jaffrelot Inizan
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  4. Christian Borgs
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  5. Jennifer T. Chayes
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  6. Omar M. Yaghi
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Contributions

O.M.Y. contributed to writing, review and editing, and supervision. Z.Z. contributed to writing, review and editing, and graphics. N.R., T.J.I., J.T.C. and C.B. contributed to review and editing. Z.Z. used ChatGPT-4 for grammar and typos checking during the review and editing of this manuscript. All authors have read, corrected and verified all information presented in this manuscript.

Corresponding author

Correspondence to Omar M. Yaghi.

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Nature Reviews Materials thanks Seyed Mohamad Moosavi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

API

(Application Programming Interface). A set of rules and protocols that allow different software applications to communicate and share data or commands.

CIFs

(Crystallographic Information Files). A standardized text file format that records crystal structure data, including atomic coordinates and unit cell parameters, enabling consistent sharing of crystal structures.

JSON

(JavaScript Object Notation). A lightweight, text-based format used to structure, store and transfer data between systems in a human-readable manner.

LLM-in-the-loop

A workflow in which a large language model (LLM) continuously participates and provides input, just as a human expert would in a ‘human-in-the-loop’ scenario. The agent may propose actions, analyse data or suggest refinements, and then adapt its guidance based on feedback from experimental results, computational tools or human researchers.

Neural networks

A computational model inspired by the structure of the human brain, composed of layers of interconnected nodes (neurons) that process and learn patterns from data.

SMILES

(Simplified Molecular Input Line Entry System). A textual notation for representing chemical structures, allowing for easy storage, manipulation and computational handling of molecular information.

Tokens

The smallest units of text (such as words, parts of words or symbols) that a language model processes and generates during text analysis and processing.

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Zheng, Z., Rampal, N., Inizan, T.J. et al. Large language models for reticular chemistry. Nat Rev Mater 10, 369–381 (2025). https://doi.org/10.1038/s41578-025-00772-8

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