High- and medium-entropy alloys can exhibit exceptional sets of mechanical and catalytic properties underpinned by the vast array of local atomic arrangements. Among the key features governing properties is the self-assembly of specific atomic neighborhoods, a process known as chemical short-range ordering (CSRO). However, a comprehensive understanding of CSRO is hindered by the lack of direct three-dimensional imaging of atomic-scale neighborhoods. Although transmission electron microscopy (TEM) and atom probe tomography (APT) have been extensively applied independently, the inherent trade-offs between spatial resolutions and chemical sensitivity of each technique limit their effectiveness in fully resolving CSRO. Here, we demonstrate that native oxide layers from air exposure of the sample crystallize under electron beam irradiation, creating overlapping signal in the diffraction patterns that complicate CSRO identification. Correlative machine learning (ML)-enhanced APT analysis disentangles the contribution from native oxide and confirms L1 1 -type CSRO with Cr-Cr avoidance on {111} planes. ML-APT provides complementary quantitative 3D metrics, enabling cross-validation and resolving discrepancies in TEM observations arising from projection effects and surface oxidation. Our analytical framework reconciles conflicting prior CSRO reports in CoCrNi and establishes a general strategy for reliable atomic-scale characterization of short-range order in complex materials.
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Li et al. (2026) studied this question.
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