| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Berkels, Benjamin
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2024The effect of Laves phases and nano-precipitates on the electrochemical corrosion resistance of Mg-Al-Ca alloys under alkaline conditionscitations
- 2023A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data
- 2023Laves phases in Mg-Al-Ca alloys and their effect on mechanical properties
- 2023Tailoring the Plasticity of Topologically Close‐packed Phases via the Crystals’ Fundamental Building Blockscitations
- 2023Constructing phase diagrams for defects by correlated atomic-scale characterizationcitations
- 2023Tailoring the Plasticity of Topologically Close‐Packed Phases via the Crystals’ Fundamental Building Blockscitations
- 2023A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Datacitations
- 2020Multi-modal and multi-scale non-local means method to analyze spectroscopic datasetscitations
- 2018Atomic-Scale Insights into the Oxidation of Aluminumcitations
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article
A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data
Abstract
Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify chemically distinct domains in a semi automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real space segmentation via a density based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate like parts by an unsupervised approach based on principle component analysis, or a U-Net based semantic segmentation trained on the former. Following the chemical and geometric analysis, detailed chemical distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped without resorting to the initial voxelization.