X-ray
diffraction (XRD) data analysis can be a time-consuming
and
laborious task. Deep neural network (DNN) based models trained with
synthetic XRD patterns have been proven to be a highly efficient,
accurate, and automated method for analyzing common XRD data collected
from solid samples in ambient environments. However, it remains unclear
whether synthetic XRD-based models can be effective in solving micro(μ)-XRD
mapping data for in situ experiments involving liquid phases, which
always have lower quality and significant artifacts. In this study,
we collected μ-XRD mapping data from a LaCl3-calcite
hydrothermal fluid system and trained two categories of models to
analyze the experimental XRD patterns. The models trained solely with
synthetic XRD patterns showed low accuracy (as low as 64%) when solving
experimental μ-XRD mapping data. However, the accuracy of the
DNN models significantly improved (90% or above) when we trained them
with a data set containing both synthetic and a small number of labeled
experimental μ-XRD patterns. This study highlights the importance
of labeled experimental patterns in training DNN models to solve μ-XRD
mapping data from in situ experiments involving liquid phases.
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