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

Kihoon Bang, Doosun Hong, Youngtae Park, Donghun Kim (), Sang Soo Han () and Hyuck Mo Lee ()
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Kihoon Bang: Korea Advanced Institute of Science and Technology (KAIST)
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)

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38758-1

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