EXTENDED ABSTRACT: In solids, chemical short-range order (CSRO) refers to the self-organisation of atoms of certain species occupying speciffc crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. CSRO is typically characterized indirectly, using volume-averaged (e.g. X-ray/ neutron scattering) or through projection microscopy techniques that fail to capture the complex, 3D atomistic architectures. Quantitative assessment of CSRO and concrete structure-property relationships have remained so far unachievable. Herein, we showcase 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 series of metallic materials, including Fe-Al, Fe-Ga, and CoCrNi medium-entropy alloys. We reveal multiple CSRO conffgurations, with their formation supported by state-of-the-art Monte-Carlo simulations. Quantitative analysis of these CSROs allows us to establish relationships between processing parameters and physical properties. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in a vastarray of materials and help design future high-performance materials.

Keywords: Atom probe; Local chemical ordering; Medium-entropy alloys

REFERENCES:

[1] Yue Li*, et al. Advanced Materials, (2024) 2407564.

[2] Yue Li*, et al. Nature Communications, 14, (2023) 7410.