Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles
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). Here...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-38758-1 |
| _version_ | 1797817878528917504 |
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| author | Kihoon Bang Doosun Hong Youngtae Park Donghun Kim Sang Soo Han Hyuck Mo Lee |
| author_facet | Kihoon Bang Doosun Hong Youngtae Park Donghun Kim Sang Soo Han Hyuck Mo Lee |
| author_sort | Kihoon Bang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T09:00:56Z |
| format | Article |
| id | doaj.art-ed7d04a9ac564311bc7c1502879bd4bb |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T09:00:56Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-ed7d04a9ac564311bc7c1502879bd4bb2023-05-28T11:22:30ZengNature PortfolioNature Communications2041-17232023-05-0114111110.1038/s41467-023-38758-1Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticlesKihoon Bang0Doosun Hong1Youngtae Park2Donghun Kim3Sang Soo Han4Hyuck Mo Lee5Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST)Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST)Computational Science Research Center, Korea Institute of Science and Technology (KIST)Computational Science Research Center, Korea Institute of Science and Technology (KIST)Department of Materials Science and Engineering, 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.https://doi.org/10.1038/s41467-023-38758-1 |
| spellingShingle | Kihoon Bang Doosun Hong Youngtae Park Donghun Kim Sang Soo Han Hyuck Mo Lee Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles Nature Communications |
| title | Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles |
| title_full | Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles |
| title_fullStr | Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles |
| title_full_unstemmed | Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles |
| title_short | Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles |
| title_sort | machine learning enabled exploration of the electrochemical stability of real scale metallic nanoparticles |
| url | https://doi.org/10.1038/s41467-023-38758-1 |
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