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The value of negative results in data-driven catalysis research

Nature Catalysis volume 6, pages 108–111 (2023)Cite this article

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Data science and machine learning have the potential to accelerate the discovery of effective catalysts; however, these approaches are currently held back by the issue of negative results. This Comment highlights the value of negative data by assessing the bottlenecks in data-driven catalysis research and presents a vision for a way forwards.

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Fig. 1: Negative results issue and potential solutions in data-driven catalysis research.

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Acknowledgements

We acknowledge funding from the Japan Science and Technology Agency (JST) CREST (grant number JPMJCR17P2). We thank P. Chammingkwan from Japan Advanced Institute of Science and Technology for the graphical design.

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Authors and Affiliations

  1. Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Toshiaki Taniike

  2. Department of Chemistry, Hokkaido University, Sapporo, Japan

    Keisuke Takahashi

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  1. Toshiaki Taniike
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  2. Keisuke Takahashi
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T.T. and K.T. wrote this Comment together.

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Correspondence to Toshiaki Taniike or Keisuke Takahashi.

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The authors declare no competing interests.

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Nature Catalysis thanks David Linke, Jochen Lauterbach and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Taniike, T., Takahashi, K. The value of negative results in data-driven catalysis research. Nat Catal 6, 108–111 (2023). https://doi.org/10.1038/s41929-023-00920-9

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