Machine Learning‐Enabled Tomographic Imaging of Chemical Short‐Range Atomic Ordering
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.
Authors
- Huaide Zhang
- Jörg Neugebauer (ORCID: https://orcid.org/0000-0002-7903-2472)
- Fritz Körmann (ORCID: https://orcid.org/0000-0003-3050-6291)
- Ye Wei (ORCID: https://orcid.org/0000-0003-1965-2298)
- Andreas Marek (ORCID: https://orcid.org/0000-0001-5403-7528)
- Matthias Wuttig (ORCID: https://orcid.org/0000-0003-1498-1025)
- Yilun Gong (ORCID: https://orcid.org/0000-0002-8088-2562)
- Zongrui Pei (ORCID: https://orcid.org/0000-0003-0748-4629)
- Baptiste Gault (ORCID: https://orcid.org/0000-0002-4934-0458)
- Markus Rampp (ORCID: https://orcid.org/0000-0001-8177-8698)
- Timoteo Colnaghi (ORCID: https://orcid.org/0000-0002-2380-7128)
- Yue Li (ORCID: https://orcid.org/0000-0003-3377-6676)
- Yu Zhen Yuan (ORCID: https://orcid.org/0000-0002-3148-6600)
- Zhangwei Wang (ORCID: https://orcid.org/0000-0002-0548-3799)
- Sheuly Ghosh (ORCID: https://orcid.org/0000-0002-9786-0271)
- Siyuan Zhang (ORCID: https://orcid.org/0009-0004-7381-0028)
- Min Sup Song
- Bin Gan (ORCID: https://orcid.org/0009-0009-5270-6703)
Institutions
- Federal Institute For Materials Research and Testing (DE)
- Central South University (CN)
- University of Oxford (GB)
- Max-Planck-Institut für Nachhaltige Materialien (DE)
- Max Planck Computing and Data Facility (DE)
- Imperial College London (GB)
- École Polytechnique Fédérale de Lausanne (CH)
- New York University (US)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Advanced Materials
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1002/adma.202407564
- Citations
- 18
- Primary Topic
- Advanced Materials Characterization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.32
Funders
- Alexander von Humboldt-Stiftung
- Deutsche Forschungsgemeinschaft
- Central South University
- Natural Science Foundation of Hunan Province
- State Key Laboratory of Powder Metallurgy
- European Research Council