About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
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Symposium
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Characterization of Minerals, Metals and Materials 2022
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Presentation Title |
Machine Learning Enabled Atom Tomographic Imaging of Chemical Short-range Order in Fe-18Al
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Author(s) |
Yue Li, Zhangwei Wang, Leigh T. Stephenson, Baptiste Gault |
On-Site Speaker (Planned) |
Yue Li |
Abstract Scope |
Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix. These particular atomic neighbourhoods can modify the mechanical and functional performances of materials. CSRO is typically characterized indirectly, using volume-averaged (e.g. X-ray/neutron scattering) or through projection (i.e. two-dimensional) microscopy techniques that fail to capture the complex, three-dimensional atomistic architectures. Quantitative assessment of CSRO and concrete structure-property relationships remain 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 (at.%) solid solution alloy that sees anomalous property changes upon heat treatment. After validating our method against artificial data for ground truth, we unearth non-statistical B2-CSRO (FeAl) instead of the generally-expected D03-CSRO (Fe3Al). We propose quantitative correlations among annealing temperature, CSRO, and the nano-hardness and electrical resistivity, supported by atomistic simulations. 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. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Characterization, Other |