Feature space of XRD patterns constructed by autoencorder
Abstract
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 autoencoder technique to construct a feature space for the XRD peak patterns. Individual XRD patterns are projected onto a single point in the twodimensional 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 higherorder peaks from the same plane index, being a heuristic finding by the power of machinelearning.
 Publication:

arXiv eprints
 Pub Date:
 May 2020
 DOI:
 10.48550/arXiv.2005.11660
 arXiv:
 arXiv:2005.11660
 Bibcode:
 2020arXiv200511660U
 Keywords:

 Physics  Computational Physics;
 Condensed Matter  Materials Science;
 Physics  Data Analysis;
 Statistics and Probability
 EPrint:
 doi:10.1002/adts.202200613