The XANES spectra used as input to models in the main text are all post-edge normalized (we address the effect of normalization in the discussion section and SI), and are featurized in two different ways in order to coax enhanced interpretability from our models. We refer to featurization based on the full 100 equally spaced values of the spectrum as "pointwise" featurization. Pointwise feature ranking illuminates which energy regions matter, but it is unclear from the ranking alone if the slopes or peaks of the spectra are what drive the predictions, or if the spectral magnitudes are the primary contributors. This motivates the use of a multiscale polynomial featurization which is new to this work. Our featurization technique (depicted in Figure ) captures information about the entire spectrum on both a coarse and fine level, describing trends in subdivided domains ranging from 2.5 eV to 12.5 eV. Because the polynomial terms correspond to the local constant, linear, quadratic, and cubic character of the spectra, tracking the importance of each coefficient conveys if the magnitude (a 0 ) of µ(E) is most important for the prediction task, or if the local derivatives as parameterized by polynomial fits (a 1 , a 2 , a 3 ) are what matter. This disambiguates which aspects of the spectrum are contributing to the decisions of the model. We additionally add to the vector the white line energy, or peak absorption energy. We find that trends in feature importance are highly dependent on the element in question, the prediction task at hand, and the normalization. Crucially, we find that featurizing the spectra with overlapping polynomial fits usually results in similar or improved model performance and improved interpretability compared to pointwise featurization.