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Rapid detection of rare events from in situ X-ray diffraction data using machine learning

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form.

Data and Resources

Cite this as

Weijian Zheng, Jun-Sang Park, Peter Kenesei, Ahsan Ali, Zhengchun Liu, Ian T. Foster, Nicholas Schwarz, Rajkumar Kettimuthu, Antonino Miceli, Hemant Sharma (2024). Dataset: Rapid detection of rare events from in situ X-ray diffraction data using machine learning. https://doi.org/10.57702/bb14rr84

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2312.03989
Author Weijian Zheng
More Authors
Jun-Sang Park
Peter Kenesei
Ahsan Ali
Zhengchun Liu
Ian T. Foster
Nicholas Schwarz
Rajkumar Kettimuthu
Antonino Miceli
Hemant Sharma
Homepage https://doi.org/10.1107/S2052252521001258