  It would be a natural expectation that only major peaks, not all of them,
would make an important contribution to the characterization of the XRD
pattern. We developed a scheme that can identify which peaks are relavant to
what extent by using auto-encoder technique to construct a feature space for
the XRD peak patterns. Individual XRD patterns are projected onto a single
point in the two-dimensional feature space constructed using the method. If the
point is significantly shifted when a peak of interest is masked, then we can
say the peak is relevant for the characterization represented by the point on
the space. In this way, we can formulate the relevancy quantitatively. By using
this scheme, we actually found such a peak with a significant peak intensity
but low relevancy in the characterization of the structure. The peak is not
easily explained by the physical viewpoint such as the higher-order peaks from
the same plane index, being a heuristic finding by the power of
machine-learning.
