AI Article Synopsis
- Chemical short-range order (CSRO) in solids involves the arrangement of atoms in specific crystal sites, which can influence the materials' mechanical and functional properties.
- The study uses machine learning-enhanced atom probe tomography (APT) to analyze CSRO in a CoCrNi medium-entropy alloy, revealing various configurations and their formation through simulations.
- This detailed understanding of CSRO helps establish connections between processing conditions and material properties, aiding in the design of advanced materials by manipulating atomic structures.
Article 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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| http://dx.doi.org/10.1002/adma.202407564 | DOI Listing |
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