Speaker
Description
In solids, chemical short-range order (CSRO) refers to the self-organisation 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. 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 configurations, 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 vast array of materials and help design future high-performance materials.
