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Fewer false positives in electrocatalytic nitrogen reduction by synergizing theory and experiment

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The electrocatalytic nitrogen reduction reaction is a promising alternative to the Haber–Bosch process. However, the reproducibility and reliability of this process suffer from the persistence of false positives. Computational tools have the potential to alleviate this issue but several challenges must be addressed.

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Fig. 1: False positives in the electrocatalytic nitrogen reduction reaction (eNRR) and computational approaches to eliminating them.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (22273076 to T.W.) and the National Key Research and Development Program of China (2022YFA0911900); T.W. acknowledges start-up packages from Westlake University and the Kunpeng research fund from Zhejiang Province. We thank the Research Center for Industries of the Future (RCIF) at Westlake University for supporting this work. We thank Westlake University HPC Center for computation support.

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Authors and Affiliations

  1. Center of Artificial Photosynthesis for Solar Fuels and Department of Chemistry, School of Science and Research Center for Industries of the Future, Westlake University, Hangzhou, China

    Gaomou Xu & Tao Wang

  2. Institute of Natural Sciences, Westlake Institute for Advanced Study, Hangzhou, China

    Gaomou Xu & Tao Wang

  3. Division of Solar Energy Conversion and Catalysis at Westlake University, Zhejiang Baima Lake Laboratory, Hangzhou, China

    Tao Wang

Authors
  1. Gaomou Xu
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  2. Tao Wang
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Contributions

T.W. conceived the research; G.M.X. wrote the first draft; all authors edited and approved the manuscript.

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Correspondence to Tao Wang.

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The authors declare no competing interests.

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Nature Computational Science thanks Yaqiong Su and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Xu, G., Wang, T. Fewer false positives in electrocatalytic nitrogen reduction by synergizing theory and experiment. Nat Comput Sci 3, 994–997 (2023). https://doi.org/10.1038/s43588-023-00556-6

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