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The value of negative results in data-driven catalysis research

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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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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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).

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