Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data

Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data

The 3D nano/microstructure of materials can significantly influence their macroscopic properties. In order to enable a better understanding of such structure-property relationships, 3D microscopy techniques can be deployed, which are however often expensive in both time and costs. Often 2D imaging t...

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Bibliographic Details
Main Authors: Lukas Fuchs, Tom Kirstein, Christoph Mahr, Orkun Furat, Valentin Baric, Andreas Rosenauer, Lutz Mädler, Volker Schmidt
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
synthetic HAADF-STEM
nanoparticle aggregate
hetero-aggregate
convolutional neural network
stereological characterization
stochastic 3D model
Online Access:https://doi.org/10.1088/2632-2153/ad38fd

Internet

https://doi.org/10.1088/2632-2153/ad38fd

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