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Obtaining 3D Chemical Maps by Energy Filtered Transmission Electron Microscopy Tomography

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

Yue Li¹, Timoteo Colnaghi², Yilun Gong^1,3

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

Advanced Materials (Deerfield Beach, Fla.)

|August 13, 2024

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 intelligence atomic‐scale characterization high/medium‐entropy alloys local chemical ordering tomographic imaging

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Last Updated: Jun 17, 2025

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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.