ArticleJACS Au2026
Constant-Potential MD with Neural Network Potentials Reveals Cation Effects on CO
Article in JACS Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Self-single-atomization of active sites in electrochemical ammonia synthesis unveiled by machine learning potential.National science review · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the evolution of electrified solid-liquid interfaces during electrochemical reactions is critical but poses significant challenges due to the difficulty in capturing their dynamic behavior with high temporal resolution over extended time scales. Here, we present a constant potential reactor framework that enables ab initio-accurate simulations of electrochemical reactions, providing real-time, atomic-scale insights into the evolution of electrified interfaces. By integrating an enhanced-sampling active learning protocol, our approach leverages scalable neural network potentials trained on high-throughput density functional theory computations within an explicit-implicit hybrid solvent model. This framework uncovers key mechanistic insights, such as the intrinsic role of alkali metal cations in promoting CO
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Registered trials
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