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Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis

Author

Listed:
  • Manu Suvarna

    (Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich)

  • Alain Claude Vaucher

    (IBM Research Europe)

  • Sharon Mitchell

    (Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich)

  • Teodoro Laino

    (IBM Research Europe)

  • Javier Pérez-Ramírez

    (Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich)

Abstract

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model’s adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.

Suggested Citation

  • Manu Suvarna & Alain Claude Vaucher & Sharon Mitchell & Teodoro Laino & Javier Pérez-Ramírez, 2023. "Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43836-5
    DOI: 10.1038/s41467-023-43836-5
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    References listed on IDEAS

    as
    1. Chris Stokel-Walker & Richard Van Noorden, 2023. "What ChatGPT and generative AI mean for science," Nature, Nature, vol. 614(7947), pages 214-216, February.
    2. Alain C. Vaucher & Federico Zipoli & Joppe Geluykens & Vishnu H. Nair & Philippe Schwaller & Teodoro Laino, 2020. "Automated extraction of chemical synthesis actions from experimental procedures," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Sharon Mitchell & Javier Pérez-Ramírez, 2020. "Single atom catalysis: a decade of stunning progress and the promise for a bright future," Nature Communications, Nature, vol. 11(1), pages 1-3, December.
    4. Alain C. Vaucher & Philippe Schwaller & Joppe Geluykens & Vishnu H. Nair & Anna Iuliano & Teodoro Laino, 2021. "Inferring experimental procedures from text-based representations of chemical reactions," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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