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nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems

Abstract:

We describe nNPipe for the automated analysis of morphologically diverse catalyst materials. Automated imaging routines and direct-electron detectors have enabled the collection of large data stacks over a wide range of sample positions at high temporal resolution. Simultaneously, traditional image analysis approaches are slow and hence unsuitable for large data stacks and consequently, researchers have progressively turned towards machine learning and deep learning approaches. Previous studi...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41524-022-00949-7

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Institution:
University of Oxford
Division:
MPLS
Department:
Materials
Role:
Author
Publisher:
Springer Nature
Journal:
npj Computational Materials More from this journal
Volume:
9
Article number:
18
Publication date:
2023-02-04
Acceptance date:
2022-12-12
DOI:
ISSN:
2057-3960
Language:
English
Keywords:
Pubs id:
1314972
Local pid:
pubs:1314972
Deposit date:
2022-12-14

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