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

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 h...

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Autores principales: Suvarna, Manu, Vaucher, Alain Claude, Mitchell, Sharon, Laino, Teodoro, Pérez-Ramírez, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693572/
https://www.ncbi.nlm.nih.gov/pubmed/38042926
http://dx.doi.org/10.1038/s41467-023-43836-5
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author Suvarna, Manu
Vaucher, Alain Claude
Mitchell, Sharon
Laino, Teodoro
Pérez-Ramírez, Javier
author_facet Suvarna, Manu
Vaucher, Alain Claude
Mitchell, Sharon
Laino, Teodoro
Pérez-Ramírez, Javier
author_sort Suvarna, Manu
collection PubMed
description 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.
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spelling pubmed-106935722023-12-04 Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis Suvarna, Manu Vaucher, Alain Claude Mitchell, Sharon Laino, Teodoro Pérez-Ramírez, Javier Nat Commun Article 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. Nature Publishing Group UK 2023-12-02 /pmc/articles/PMC10693572/ /pubmed/38042926 http://dx.doi.org/10.1038/s41467-023-43836-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Suvarna, Manu
Vaucher, Alain Claude
Mitchell, Sharon
Laino, Teodoro
Pérez-Ramírez, Javier
Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
title Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
title_full Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
title_fullStr Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
title_full_unstemmed Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
title_short Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
title_sort language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693572/
https://www.ncbi.nlm.nih.gov/pubmed/38042926
http://dx.doi.org/10.1038/s41467-023-43836-5
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