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preprint · 2026

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

Atomistic simulations of electrochemical interfaces remain challenging due to the long time scales required to adequately sample the structure of the electri...

0 citationsDOI: https://doi.org/10.48550/arxiv.2602.22931Open access
Benchmarking short-range machine learning potentials for atomistic simulations of metal/electrolyte interfaces