most citedBenchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces

1 citations · 1 across the 1 of their papers we have counts for

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physics.chem-ph2026★ 1 cited

Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces

Lucas B. T. de Kam, Jia-Xin Zhu, Ankit Mathanker +2

Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electric double layer. The eme…

physics.chem-ph2024

Electrolyte effects on the alkaline hydrogen evolution reaction: a mean-field approach

Lucas B. T. de Kam, Thomas L. Maier, Katharina Krischer

This paper introduces the combination of an advanced double-layer model with electrochemical kinetics to explain electrolyte effects on the alkaline hydrogen evolution reaction. It…