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Scientific researchers frequently use the in situ synchrotron high-energypowder X-ray diffraction (XRD) technique to examine the crystallographicstructures of materials in functional devices such as rechargeable batterymaterials. We propose a method for identifying artifacts in experimental XRDimages. The proposed method uses deep learning convolutional neural networkarchitectures, such as tunable U-Nets to identify the artifacts. In particular,the predicted artifacts are evaluated against the corresponding ground truth(manually implemented) using the overall true positive rate or recall. Theresult demonstrates that the U-Nets can consistently produce great recallperformance at 92.4% on the test dataset, which is not included in thetraining, with a 34% reduction in average false positives in comparison to theconventional method. The U-Nets also reduce the time required to identify andseparate artifacts by more than 50%. Furthermore, the exclusion of theartifacts shows major changes in the integrated 1D XRD pattern, enhancingfurther analysis of the post-processing XRD data.

Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images