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Embracing data science in catalysis research

Nature Catalysis (2024)Cite this article

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Abstract

Accelerating catalyst discovery and development is of paramount importance in addressing the global energy, sustainability and healthcare demands. The past decade has witnessed significant momentum in harnessing data science concepts in catalysis research to aid the aforementioned cause. Here we comprehensively review how catalysis practitioners have leveraged data-driven strategies to solve complex challenges across heterogeneous, homogeneous and enzymatic catalysis. We delineate all studies into deductive or inductive modes, and statistically infer the prevalence of catalytic tasks, model reactions, data representations and choice of algorithms. We highlight frontiers in the field and knowledge transfer opportunities among the catalysis subdisciplines. Our critical assessment reveals a glaring gap in data science exploration in experimental catalysis, which we bridge by elaborating on four pillars of data science, namely descriptive, predictive, causal and prescriptive analytics. We advocate their adoption into routine experimental workflows and underscore the importance of data standardization to spur future research in digital catalysis.

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Fig. 1: Overview of research trends and ML applications in catalysis.
Fig. 2: Navigating the landscape of ML in catalysis.
Fig. 3: Summary of major open-source databases in catalysis.
Fig. 4: Establishing structure–property–performance relationship through ML.
Fig. 5: Advanced AI frameworks in catalysis.
Fig. 6: Four pillars of data-driven catalysis.
Fig. 7: Life cycle of data-driven catalysis.
Fig. 8: Accelerating innovation in characterization workflows.

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Acknowledgements

This study was created as part of NCCR Catalysis (grant no. 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation. We thank C. Ko, M. E. Usteri, T. Zou and P. Preikschas for fruitful discussions on the manuscript and help with illustrations.

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Authors and Affiliations

  1. Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland

    Manu Suvarna & Javier Pérez-Ramírez

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  1. Manu Suvarna
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  2. Javier Pérez-Ramírez
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Contributions

M.S. and J.P.-R. conceived the project. M.S. led the data collection and analysis efforts, and wrote the manuscript. J.P.-R. supervised the project, wrote the manuscript, and managed resources and funding. Both authors provided input to the manuscript and approved the final version.

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Correspondence to Javier Pérez-Ramírez.

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Supplementary Information

Supplementary Notes 1 and 2, Tables 1–3 and Fig. 1.

Source data

41929_2024_1150_MOESM2_ESM.xlsx

Source Data Fig. 1a Raw data for creating timeline plot of research trends in data-driven catalysis. Source Data Fig. 1c Raw data for creating alluvial plots linking catalysis subdisciplines with different tasks and modes. Source Data Fig. 2a Raw data for network plot mapping relations of deductive tasks based on catalysis type. Source Data Fig. 2b Raw data for network plot mapping relations of deductive tasks based on driving. Source Data Fig. 4 Raw data for establishing structure-property-performance relations through ML.

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Suvarna, M., Pérez-Ramírez, J. Embracing data science in catalysis research. Nat Catal (2024). https://doi.org/10.1038/s41929-024-01150-3

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