ArticleNature communications2025
Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Self-single-atomization of active sites in electrochemical ammonia synthesis unveiled by machine learning potential.National science review · 2026Article
- Asymmetric Ru─O sites in self-activated catalysts for efficient electrochemical methanol oxidation and industrial-scale hydrogen generation.Science advances · 2026Article
- Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice.Small methods · 2026Review
- Article
- Electrochemical potential-driven water dynamics control CONature communications · 2025Article
- Multicomponent Double-Hybrid Density Functional Theory.Journal of chemical theory and computation · 2025Article
- Periodic Constrained Nuclear-Electronic Orbital Density Functional Theory for Nuclear Quantum Effects: Method Development and Application to Hydrogen Adsorption on Pt(111).Journal of chemical theory and computation · 2025Article
- Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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