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Rethinking chemical research in the age of large language models

Nature Computational Science volume 5, pages 715–726 (2025)Cite this article

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Abstract

Large language models (LLMs) offer opportunities for advancing chemical research, including planning, optimization, data analysis, automation and knowledge management. Deploying LLMs in active environments, where they interact with tools and data, can greatly enhance their capabilities. However, challenges remain in evaluating their performance and addressing ethical issues such as reproducibility, data privacy and bias. Here we discuss ongoing and potential integrations of LLMs in chemical research, highlighting existing challenges to guide the effective use of LLMs as active scientific partners.

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Fig. 1: Relationships between different kinds of high-level operation that LLMs can perform and be evaluated on.
Fig. 2: An integrated research framework for automated scientific discovery.
Fig. 3: An overview of the interplay between LLMs and tools.
Fig. 4: Existing usage and deployment challenges of LLMs in chemical research.

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Acknowledgements

We thank J. Kitchin and E. Spotte-Smith (both at CMU Chemical Engineering) for their vital feedback while writing the manuscript. We gratefully acknowledge the financial support by the National Science Foundation Center for Computer-Assisted Synthesis (NSF C-CAS, grant no. 2202693). R.M. thanks CMU and its benefactors for the 2024–25 Tata Consulting Services Presidential Fellowship. J.E.R. thanks NSF and C-CAS for the 2024-25 DARE Fellowship. L.C.G. thanks the CMU Mellon College of Sciences for the Automated Sciences Postdoctoral Fellowship. Any opinions, findings and conclusions or recommendations expressed in this perspective are those of the author(s) and do not necessarily reflect the views of any of the funding sources.

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Authors and Affiliations

  1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Robert MacKnight, Daniil A. Boiko, Théo A. Neukomm & Gabe Gomes

  2. Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA

    Jose Emilio Regio, Liliana C. Gallegos, Théo A. Neukomm & Gabe Gomes

  3. Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Théo A. Neukomm

  4. Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Gabe Gomes

  5. Wilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, PA, USA

    Gabe Gomes

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Correspondence to Gabe Gomes.

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G.G. and R.M. are co-founders of evals, a consultancy firm for scientific evaluations of frontier AI models. The other authors declare no competing interests.

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MacKnight, R., Boiko, D.A., Regio, J.E. et al. Rethinking chemical research in the age of large language models. Nat Comput Sci 5, 715–726 (2025). https://doi.org/10.1038/s43588-025-00811-y

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