Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data-Reference-Cited by-同舟云学术

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Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data

Published:2022-03 Issue: Volume:226 Page:117633
ISSN:1359-6454
Container-title:Acta Materialia
language:en
Short-container-title:Acta Materialia

Author:

Zhou Xuyang,Wei Ye,Kühbach Markus,Zhao Huan,Vogel Florian,Darvishi Kamachali Reza,Thompson Gregory B.,Raabe Dierk,Gault Baptiste

Publisher

Elsevier BV

Subject

Metals and Alloys,Polymers and Plastics,Ceramics and Composites,Electronic, Optical and Magnetic Materials

Reference112 articles.

1. Interfaces in Crystalline Materials;Sutton,1995

2. Ultrahigh Strength and High Electrical Conductivity in Copper;Lu;Science,2004

3. Phase Transformations in Metals and Alloys;Porter,2009

4. A diffuse interface model of grain boundary faceting;Abdeljawad;J. Appl. Phys.,2016

5. Segregation assisted grain boundary precipitation in a model Al-Zn-Mg-Cu alloy;Zhao;Acta Mater,2018

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1. 3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures;Acta Materialia;2024-10

2. A MATLAB Toolbox for Findable, Accessible, Interoperable, and Reusable Atom Probe Data Science;Microscopy and Microanalysis;2024-06-17

3. XGBoost algorithm-assisted prediction of amorphous formability of Fe-based nanocrystalline precursor;Intermetallics;2024-03

4. Grain boundary segregation predicted by quantum-accurate segregation spectra but not by classical models;Acta Materialia;2024-03

5. Computer vision assisted decomposition analysis of atom probe tomography data;Journal of Physics D: Applied Physics;2023-08-29

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