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This is a Quantitative Study study.

October 17, 2025Open Access

Short-range machine-learning potentials for aqueous electrolyte solutions

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Benchmarking the MACE architecture improves accuracy and efficiency in sodium chloride simulations, suggesting minimal models may stabilize outcomes.

The predictive accuracy of machine-learning potentials significantly impacts simulation stability, emphasizing their importance.
Benchmarking the MACE architecture on sodium chloride shows that model size and equivariance greatly affect efficiency.
System-specific machine-learning potentials offer a more sustainable alternative for large-scale simulations of electrolyte solutions.
The study highlights a trade-off between computational cost and the ability to capture long-range interactions in models.

Stein et al. (2025) studied this question.

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Benchmarking the MACE architecture improves accuracy and efficiency in sodium chloride simulations, suggesting minimal models may stabilize outcomes.