ReviewThe journal of physical chemistry. C, Nanomaterials and interfaces2026
How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes.
Review in The journal of physical chemistry. C, Nanomaterials and interfaces, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrochemistry lies at the heart of modern energy technologies, yet connecting atomic-level insights to macroscopic performance remains an enduring challenge. Quantum-based simulations, such as density functional theory, have illuminated many fundamental processes, but their reach is limited by the complexity of real electrochemical environments. Bridging these scales requires a new conceptual framework that can expose the hidden connections between theory and experiment. Here, we argue that the thoughtful integration of artificial intelligence (AI) can transform electrochemical research by unifying theory, experiment, and data-driven inference. AI-assisted frameworks can accelerate convergence between computation and experiment, revealing hidden physical relationships and enabling closed-loop discovery. Realizing this vision will require developing transparent, interpretable AI models that earn the same scientific trust as human reasoning, unlocking deeper understanding and innovation across the electrochemical sciences.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.