Machine-learning-based atom probe crystallographic analysis
Author:
Funder
DFG
Publisher
Elsevier BV
Subject
Instrumentation,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Reference43 articles.
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3. The anatomy of grain boundaries: their structure and atomic-level solute distribution;Yao;Scr. Mater.,2013
4. Atom probe crystallography: atomic-scale 3-D orientation mapping;Araullo-Peters;Scr. Mater.,2012
5. Atom probe crystallography: characterization of grain boundary orientation relationships in nanocrystalline aluminium;Moody;Ultramicroscopy,2011
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1. Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography;Nature Communications;2023-11-16
2. Computer vision assisted decomposition analysis of atom probe tomography data;Journal of Physics D: Applied Physics;2023-08-29
3. Revealing latent pole and zone line information in atom probe detector maps using crystallographically correlated metrics;Ultramicroscopy;2023-01
4. Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data;Acta Materialia;2022-03
5. Reflections on the Spatial Performance of Atom Probe Tomography in the Analysis of Atomic Neighborhoods;Microscopy and Microanalysis;2021-10-20
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