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

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

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Machine learning potentials for electrified interfaces

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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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Thermodynamically Consistent Machine Learning Potentials for Constant-Potential Simulations

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Simultaneous learning of static and dynamic charges

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Cooperative Effects of Active Sites in Single-Atom Catalysts

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Electrified metal–water interfaces at constant potential: Data-efficient transfer learning for machine-learning-based molecular dynamics

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Molecular Dynamics Simulations of Graphene and Graphene-Derived Ionic Liquid Supercapacitors: A Review of Methods and Applications

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Atomistic Modeling for Electrochemical Reactions: From the State of the Art to a Strategic Roadmap for Future Research within EU-CONCERT

Katharina Doblhoff-Dier, Bernardo Ballotta, Luigi Bonati, Vanessa J. Bukas, Federico Calle-Vallejo, Clotilde S. Cucinotta, Kai S. Exner, Max García-Melchor, Livia Giordano, Karoliina Honkala, Nicolas G. Hörmann, Georg Kastlunger, Marko M. Melander, Zehra B. Öcal, Arianit A. Reka, Stephan N. Steinmann, Matthias Vandichel, Jia-Xin Zhu, and Giancarlo Cicero

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Articles Citing

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

Machine learning potentials for electrified interfaces

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

Thermodynamically Consistent Machine Learning Potentials for Constant-Potential Simulations

Simultaneous learning of static and dynamic charges

Cooperative Effects of Active Sites in Single-Atom Catalysts

Electrified metal–water interfaces at constant potential: Data-efficient transfer learning for machine-learning-based molecular dynamics

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

New Frontiers in Single-Atom Catalysis: Active-Site Dynamics and AI-Enabled Design

Molecular Dynamics Simulations of Graphene and Graphene-Derived Ionic Liquid Supercapacitors: A Review of Methods and Applications

Atomistic Modeling for Electrochemical Reactions: From the State of the Art to a Strategic Roadmap for Future Research within EU-CONCERT

Embrace the Mess: Theory for Disordered Surfaces of Working Catalysts

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