Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach
Abstract Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. However, consideration of the quantum nature of transferring protons under an exact grand...
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58871-7 |
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| author | Menglin Sun Bin Jin Xiaolong Yang Shenzhen Xu |
| author_facet | Menglin Sun Bin Jin Xiaolong Yang Shenzhen Xu |
| author_sort | Menglin Sun |
| collection | DOAJ |
| description | Abstract Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. However, consideration of the quantum nature of transferring protons under an exact grand canonical constant potential condition is a great challenge for theoretical electrocatalysis. Here, we develop a unified computational framework to explicitly treat nuclear quantum effects (NQEs) by a sufficient grand canonical sampling, further assisted by a machine learning force field adapted for electrochemical conditions. Our work demonstrates a non-negligible impact of NQEs on PCET simulations for hydrogen evolution reaction at room temperature, and provides a physical picture that wave-like quantum characteristic of the transferring protons facilitates the particles to tunnel through classical barriers in PCET paths, leading to a remarkable activation energy reduction compared to classical simulations. Moreover, the physical insight of NQEs may reshape our fundamental understanding of other types of PCET reactions in broader scenarios of energy conversion processes. |
| format | Article |
| id | doaj-art-a6a947e8424942a8a0dc0eeb954fc9cd |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a6a947e8424942a8a0dc0eeb954fc9cd2025-08-20T02:17:52ZengNature PortfolioNature Communications2041-17232025-04-0116111310.1038/s41467-025-58871-7Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approachMenglin Sun0Bin Jin1Xiaolong Yang2Shenzhen Xu3School of Materials Science and Engineering, Peking UniversitySchool of Materials Science and Engineering, Peking UniversitySchool of Materials Science and Engineering, Peking UniversitySchool of Materials Science and Engineering, Peking UniversityAbstract Proton-coupled electron transfer (PCET) is the key step for energy conversion in electrocatalysis. Atomic-scale simulation acts as an indispensable tool to provide a microscopic understanding of PCET. However, consideration of the quantum nature of transferring protons under an exact grand canonical constant potential condition is a great challenge for theoretical electrocatalysis. Here, we develop a unified computational framework to explicitly treat nuclear quantum effects (NQEs) by a sufficient grand canonical sampling, further assisted by a machine learning force field adapted for electrochemical conditions. Our work demonstrates a non-negligible impact of NQEs on PCET simulations for hydrogen evolution reaction at room temperature, and provides a physical picture that wave-like quantum characteristic of the transferring protons facilitates the particles to tunnel through classical barriers in PCET paths, leading to a remarkable activation energy reduction compared to classical simulations. Moreover, the physical insight of NQEs may reshape our fundamental understanding of other types of PCET reactions in broader scenarios of energy conversion processes.https://doi.org/10.1038/s41467-025-58871-7 |
| spellingShingle | Menglin Sun Bin Jin Xiaolong Yang Shenzhen Xu Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach Nature Communications |
| title | Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach |
| title_full | Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach |
| title_fullStr | Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach |
| title_full_unstemmed | Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach |
| title_short | Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach |
| title_sort | probing nuclear quantum effects in electrocatalysis via a machine learning enhanced grand canonical constant potential approach |
| url | https://doi.org/10.1038/s41467-025-58871-7 |
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