Advanced Materials, volume 36, issue 44

Machine Learning‐Enabled Tomographic Imaging of Chemical Short‐Range Atomic Ordering

Yue Li 1
Timoteo Colnaghi 2
Yilun Gong 1, 3
Huaide Zhang 4
Yu Yuan 4
Ye Wei 5
Bin Gan 6
Min Song 7
Andreas Marek 2
Markus Rampp 2
Siyuan Zhang 1
Zongrui Pei 8
Matthias Wuttig 4
Sheuly Ghosh 1
Fritz Körmann 1, 9
Jörg Neugebauer 1
Zhangwei Wang 7
Baptiste Gault 1, 10
Show full list: 18 authors
Publication typeJournal Article
Publication date2024-08-12
scimago Q1
SJR9.191
CiteScore43.0
Impact factor27.4
ISSN09359648, 15214095
Abstract

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.

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