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Chemical short-range order (SRO) affects the distribution of elementsthroughout the solid-solution phase of metallic alloys, thereby modifying thebackground against which microstructural evolution occurs. Investigating suchchemistry-microstructure relationships requires atomistic models that act atthe appropriate length scales while capturing the intricacies of chemical bondsleading to SRO. Here we consider various approaches for the construction oftraining data sets for machine learning potentials (MLPs) for CrCoNi andevaluate their performance in capturing SRO and its effects on materialsquantities of relevance for mechanical properties, such as stacking-faultenergy and phase stability. It is demonstrated that energy accuracy on testsets often does not correlate with accuracy in capturing material properties,which is fundamental in enabling large-scale atomistic simulations of metallicalloys with high physical fidelity. Based on this analysis we systematicallyderive design principles for the rational construction of MLPs that capture SROin the crystal and liquid phases of alloys.

Capturing short-range order in high-entropy alloys with machine learning potentials