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Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks

Author

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  • Hemanth Somarajan Pillai

    (Virginia Polytechnic Institute and State University)

  • Yi Li

    (Jiangsu University)

  • Shih-Han Wang

    (Virginia Polytechnic Institute and State University)

  • Noushin Omidvar

    (Virginia Polytechnic Institute and State University)

  • Qingmin Mu

    (Virginia Polytechnic Institute and State University)

  • Luke E. K. Achenie

    (Virginia Polytechnic Institute and State University)

  • Frank Abild-Pedersen

    (SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory)

  • Juan Yang

    (Jiangsu University)

  • Gang Wu

    (University at Buffalo, The State University of New York)

  • Hongliang Xin

    (Virginia Polytechnic Institute and State University)

Abstract

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Suggested Citation

  • Hemanth Somarajan Pillai & Yi Li & Shih-Han Wang & Noushin Omidvar & Qingmin Mu & Luke E. K. Achenie & Frank Abild-Pedersen & Juan Yang & Gang Wu & Hongliang Xin, 2023. "Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36322-5
    DOI: 10.1038/s41467-023-36322-5
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    References listed on IDEAS

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    1. Miao Zhong & Kevin Tran & Yimeng Min & Chuanhao Wang & Ziyun Wang & Cao-Thang Dinh & Phil De Luna & Zongqian Yu & Armin Sedighian Rasouli & Peter Brodersen & Song Sun & Oleksandr Voznyy & Chih-Shan Ta, 2020. "Accelerated discovery of CO2 electrocatalysts using active machine learning," Nature, Nature, vol. 581(7807), pages 178-183, May.
    2. Siwen Wang & Hemanth Somarajan Pillai & Hongliang Xin, 2020. "Bayesian learning of chemisorption for bridging the complexity of electronic descriptors," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    3. Qiang Gao & Hemanth Somarajan Pillai & Yang Huang & Shikai Liu & Qingmin Mu & Xue Han & Zihao Yan & Hua Zhou & Qian He & Hongliang Xin & Huiyuan Zhu, 2022. "Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Sinsel, Simon R. & Riemke, Rhea L. & Hoffmann, Volker H., 2020. "Challenges and solution technologies for the integration of variable renewable energy sources—a review," Renewable Energy, Elsevier, vol. 145(C), pages 2271-2285.
    5. Shih-Han Wang & Hemanth Somarajan Pillai & Siwen Wang & Luke E. K. Achenie & Hongliang Xin, 2021. "Infusing theory into deep learning for interpretable reactivity prediction," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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