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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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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T.T. and K.T. wrote this Comment together.
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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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DOI: https://doi.org/10.1038/s41929-023-00920-9
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