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

doi:doi:10.57702/bb14rr84

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.

BibTex:

@dataset{Weijian_Zheng_and_Jun-Sang_Park_and_Peter_Kenesei_and_Ahsan_Ali_and_Zhengchun_Liu_and_Ian_T_Foster_and_Nicholas_Schwarz_and_Rajkumar_Kettimuthu_and_Antonino_Miceli_and_Hemant_Sharma_2024,
    abstract = {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.},
    author = {Weijian Zheng and Jun-Sang Park and Peter Kenesei and Ahsan Ali and Zhengchun Liu and Ian T. Foster and Nicholas Schwarz and Rajkumar Kettimuthu and Antonino Miceli and Hemant Sharma},
    doi = {10.57702/bb14rr84},
    institution = {No Organization},
    keyword = {'Automated detection', 'Machine learning', 'Materials science', 'Rare events', 'X-ray diffraction'},
    month = {dec},
    publisher = {TIB},
    title = {Rapid detection of rare events from in situ X-ray diffraction data using machine learning},
    url = {https://service.tib.eu/ldmservice/dataset/rapid-detection-of-rare-events-from-in-situ-x-ray-diffraction-data-using-machine-learning},
    year = {2024}
}