into system properties and behavior . Previous work has demonstrated the feasibility of classifying certain structural properties, such as oxidation number and coordination, from said spectra via ensemble learning . Recent work has also used artificial neural networks and random forests to focus on coordination alone. Comparatively less attention has been paid to data-assisted discovery of new spectrumproperty trends with XANES , although feature ranking of input values to random forests have seen success in discovering heuristics for materials behavior . Studies which focus on interpretable ML models are of particular interest to the field, as they could yield physical insights from automatically highlighted spectrum-property patterns. At a coarser level of XANES interpretation, some works have studied the importance of different spectral regions: the post-edge region of XANES spectra has been shown to be important to coordination classification from a marked drop in performance after training a model without it (see Figure of Ref. ). A split into to three energy regions before, at, and after the edge using random forests compared the relative information content in different energy regions .