Unraveling the size fluctuation and shrinkage of nanovoids during in situ radiation of Cu by automatic pattern recognition and phase field simulation

Void formation is an important aspect of irradiation response of metals. In situ transmission electron microscopy observation for void evolution during irradiation is an effective technique for studying void evolution. However, the amount of data collected during in situ studies drastically overwhel...

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Bibliographic Details
Published in:Journal of nuclear materials 2023-02, Vol.574 (C), p.154189, Article 154189
Main Authors: Nasim, M., Rayaprolu, Sreekar, Niu, T., Fan, C., Shang, Z., Li, Jin, Wang, H., El-Azab, A., Xue, Y., Zhang, X.
Format: Article
Language:English
Subjects:
In situ radiation
Machine learning
Nanovoids
Phase–field simulation
Void size fluctuation
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Summary:Void formation is an important aspect of irradiation response of metals. In situ transmission electron microscopy observation for void evolution during irradiation is an effective technique for studying void evolution. However, the amount of data collected during in situ studies drastically overwhelm the current capability for manual data analyses. Here, we used a data-driven approach where a convolutional neural network combined with greedy matching to detect and track nanovoid evolutions and migrations. This approach was able to discover the surprising phenomena of void size fluctuation and shrinkage during irradiation of Cu with pre-existing nanovoids. Phase–field simulations revealed the fundamental mechanism behind this in situ observed phenomenon of void size fluctuation.
ISSN:0022-3115
1873-4820
DOI:10.1016/j.jnucmat.2022.154189