  X-ray absorption spectroscopy (XAS) is a commonly-employed technique for
characterizing functional materials. In particular, x-ray absorption near edge
spectra (XANES) encodes local coordination and electronic information and
machine learning approaches to extract this information is of significant
interest. To date, most ML approaches for XANES have primarily focused on using
the raw spectral intensities as input, overlooking the potential benefits of
incorporating spectral transformations and dimensionality reduction techniques
into ML predictions. In this work, we focused on systematically comparing the
impact of different featurization methods on the performance of ML models for
XAS analysis. We evaluated the classification and regression capabilities of
these models on computed datasets and validated their performance on previously
unseen experimental datasets. Our analysis revealed an intriguing discovery:
the cumulative distribution function (CDF) feature achieves both high
prediction accuracy and exceptional transferability. This remarkably robust
performance can be attributed to its tolerance to horizontal shifts in spectra,
which is crucial when validating models using experimental data. While this
work exclusively focuses on XANES analysis, we anticipate that the methodology
presented here will hold promise as a versatile asset to the broader
spectroscopy community.
