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 electric double layer. The emergence of efficient, short-range machine learning interatomic potentials (MLIPs) offers a promising alternative to computationally expensive density functional theory-based molecular dynamics (DFT-MD) simulations in this regard. However, in standard periodic DFT calculations of metal surfaces, the surface charge is implicitly set by the number of counterions in the simulation cell, making it a global property that is difficult to represent with strictly local MLIPs. Here, we benchmark common MLIP architectures (DP, ACE, MACE) for charged Au/water interfaces containing solvated sodium ions. We find that MLIPs trained on datasets spanning multiple surface charge states yield inconsistent predictions of interfacial water orientation and ion distributions, although message-passing models with a larger receptive field exhibit greater robustness to training on mixed-charge datasets. In contrast, models trained on a single charge state produce consistent equilibrium interfacial properties. Finally, we assess the performance of the eSEN model trained on the recently released Open Catalyst 2025 dataset, which includes solid/liquid interfaces that span a wide range of surface charge densities. Overall, our results characterize the limitations of short-range MLIPs for simulations of electrochemical interfaces and provide practical guidance for constructing training datasets for simulations of charged metal/electrolyte interfaces.