Citing Articles For: Machine Learning the Energetics of Electrified Solid-Liquid Interfaces

Achieving all-atom molecular dynamics accuracy from the Poisson–Boltzmann method through machine learning

Ema Slejko, Amaury Coste, Tilen Potisk, Julija Zavadlav, and Matej Praprotnik

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Erasing dielectric breakdown artifacts to machine-learn charged Pt–water interfaces

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The Journal of Chemical Physics 164 174110 (2026)

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The role of interfacial excess charge in the reversibility of proton and hydroxide solvation in electrocatalysis and bipolar membranes

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Proc. Natl. Acad. Sci. U.S.A. 123 e2531938123 (2026)

Cooperative Effects of Active Sites in Single-Atom Catalysts

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J. Phys. Chem. Lett. 17 3058 (2026)

Translating experiments through first-principles simulations to unveil the microscopic electric double layer

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Accelerating discovery in computational electrocatalysis: From machine learning potentials to AI-driven workflows

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Machine Learning the Energetics of Electrified Solid-Liquid Interfaces

Achieving all-atom molecular dynamics accuracy from the Poisson–Boltzmann method through machine learning

Erasing dielectric breakdown artifacts to machine-learn charged Pt–water interfaces

Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice

The role of interfacial excess charge in the reversibility of proton and hydroxide solvation in electrocatalysis and bipolar membranes

Cooperative Effects of Active Sites in Single-Atom Catalysts

Translating experiments through first-principles simulations to unveil the microscopic electric double layer

Accelerating discovery in computational electrocatalysis: From machine learning potentials to AI-driven workflows

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