TY - JOUR A1 - Li, Yue A1 - Colnaghi, Timoteo A1 - Gong, Yilun A1 - Zhang, Huaide A1 - Yu, Yuan A1 - Wei, Ye A1 - Gan, Bin A1 - Song, Min A1 - Marek, Andreas A1 - Rampp, Markus A1 - Zhang, Siyuan A1 - Pei, Zongrui A1 - Wuttig, Matthias A1 - Ghosh, Sheuly A1 - Körmann, Fritz A1 - Neugebauer, Jörg A1 - Wang, Zhangwei A1 - Gault, Baptiste T1 - Machine learning‐enabled tomographic imaging of chemical short‐range atomic ordering N2 - In solids, chemical short‐range order (CSRO) refers to the self‐organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning‐enhanced atom probe tomography (APT) can mine the near‐atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium‐entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state‐of‐the‐art Monte‐Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic‐scale architectures. KW - Chemical short-range order (CSRO) KW - Atom probe tomography (APT) KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623777 DO - https://doi.org/10.1002/adma.202407564 SN - 1521-4095 VL - 36 IS - 44 SP - 1 EP - 9 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62377 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -