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Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles

Author

Listed:
  • Kihoon Bang

    (Korea Advanced Institute of Science and Technology (KAIST)
    Korea Institute of Science and Technology (KIST))

  • Doosun Hong

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Youngtae Park

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Donghun Kim

    (Korea Institute of Science and Technology (KIST))

  • Sang Soo Han

    (Korea Institute of Science and Technology (KIST))

  • Hyuck Mo Lee

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs). Herein, with the aim of accelerating the accurate prediction of adsorption energies, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the enhanced accuracy of the bond-type embedding approach, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. BE-CGCNN-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size. This work suggests a method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.

Suggested Citation

  • Kihoon Bang & Doosun Hong & Youngtae Park & Donghun Kim & Sang Soo Han & Hyuck Mo Lee, 2023. "Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38758-1
    DOI: 10.1038/s41467-023-38758-1
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    References listed on IDEAS

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    3. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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