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

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physics.chem-ph2026

Atomistic Modeling for Electro-chemical Reactions

K. Doblhoff-Dier, B. Ballotta, L. Bonati +16

Computational modeling plays a central role in advancing our understanding of electro-chemical reactions and may thus guide the rational development of sustainable energy technolog…

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…