Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic patterns are proved to be an automatic, accurate, high throughput method common collected from solid sample in ambient environment. However, it remains unknown that whether capable solve u-XRD mapping for in-situ experiments involving liquid phase exhibiting lower quality with significant artifacts. In this study, we LaCl3-calcite hydrothermal fluid system two categories the experimental patterns. show low accuracy (as as 64%) when solving data. DNN was significantly improved (90% or above) training them dataset containing both small number labeled This study highlighted importance on phase.