Berlin 2024 – wissenschaftliches Programm

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KFM: Fachverband Kristalline Festkörper und deren Mikrostruktur

KFM 19: Topical Session: In Situ and Multimodal Microscopy in Materials Physics (joint session MM/KFM)

KFM 19.5: Vortrag

Mittwoch, 20. März 2024, 16:45–17:00, C 130

Machine learning-enabled tomographic imaging of chemical short-range order in Fe-based alloys — •Yue Li and Baptiste Gault — Max-Planck Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237 Düsseldorf, Germany

Chemical short-range order (CSRO), describing preferential local ordering of elements within the disordered matrix, can change 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, three-dimensional atomistic architectures. Quantitative assessment of CSRO and concrete structure-property relationships have remained so far unachievable. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography to reveal three-dimensional analytical imaging of the size and morphology of multiple CSRO. We showcase our approach by addressing a long-standing question encountered in a body-centred-cubic Fe-18Al and Fe-19Ga (at.%) alloy that sees anomalous property changes upon heat treatment, supported by electron diffraction and synchrotron X-ray scattering techniques. 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.

Keywords: Atom probe tomography; Machine learning; Chemical short-range order; Fe-based alloys; Three dimensions