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In his newest paper in npj Comput. Mater., Martin pushes charge equilibration based machine learning (ML) models to their limits. We show that these models are excellent for describing systems with varying total charge states, but struggle with incorporating the effects of applied fields. This can lead to pathological behaviour in some simulations, so that more sophisticated approaches for long-range and non-local interactions are required in atomistic ML.