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Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering.

Yue Li1, Timoteo Colnaghi2, Yilun Gong1,3

  • 1Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237, Düsseldorf, Germany.

Advanced Materials (Deerfield Beach, Fla.)
|August 13, 2024
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Summary

Machine learning-enhanced atom probe tomography reveals chemical short-range order (CSRO) in CoCrNi alloys. This quantitative analysis links CSRO to material properties, aiding advanced material design.

Keywords:
artificial intelligenceatomic‐scale characterizationhigh/medium‐entropy alloyslocal chemical orderingtomographic imaging

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Area of Science:

  • Materials Science
  • Solid-State Chemistry
  • Computational Materials Science

Background:

  • Chemical short-range order (CSRO) involves atomic self-organization in solids, influencing material properties.
  • Quantitative links between CSRO morphology, atomic configurations, and material properties are not well-established.
  • Controlling CSRO is a key strategy for tailoring mechanical and functional characteristics of advanced materials.

Purpose of the Study:

  • To quantitatively analyze CSRO in a CoCrNi medium-entropy alloy using advanced characterization techniques.
  • To establish relationships between processing parameters, CSRO characteristics, and resulting material properties.
  • To demonstrate the utility of machine learning-enhanced atom probe tomography for CSRO investigation.

Main Methods:

  • Machine learning-enhanced atom probe tomography (APT) for 3D elemental and structural analysis.
  • Monte Carlo simulations to support the understanding of CSRO formation mechanisms.
  • Quantitative analysis of CSRO domain morphology, number density, and atomic configurations.

Main Results:

  • Multiple CSRO configurations were identified and quantitatively characterized in the CoCrNi alloy.
  • The formation of observed CSRO configurations was corroborated by Monte Carlo simulations.
  • Established quantitative relationships between processing parameters and material properties via CSRO analysis.

Conclusions:

  • Machine learning-enhanced APT provides unprecedented 3D quantitative insights into CSRO.
  • Understanding and controlling CSRO is crucial for designing high-performance materials.
  • This work refines strategies for materials design by manipulating atomic-scale architectures.