Short-range machine-learning potentials for aqueous electrolyte solutions

Machine-learning potentials (MLPs) extend the time and length scales of atomistic simulations, enabling the study of complex systems such as electrolyte solutions. Yet most models face a trade-off between accuracy, computational cost, and the ability to capture long-range interactions. Large foundation models promise generality but often come with substantial overhead and energy demands. In contrast, compact, system-specific models may offer a more sustainable path for large-scale simulations. Here, we benchmark the MACE architecture on aqueous sodium chloride, systematically varying model size and the level of equivariance to assess their effect on accuracy, stability, and efficiency. We find that predictive accuracy of the investigated MLPs has little influence on physical observables but is crucial for stability, highlighting the potential of minimal, dedicated models for efficient simulations of electrolyte solutions.