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X-ray absorption spectroscopy (XAS) is a commonly-employed technique forcharacterizing functional materials. In particular, x-ray absorption near edgespectra (XANES) encodes local coordination and electronic information andmachine learning approaches to extract this information is of significantinterest. To date, most ML approaches for XANES have primarily focused on usingthe raw spectral intensities as input, overlooking the potential benefits ofincorporating spectral transformations and dimensionality reduction techniquesinto ML predictions. In this work, we focused on systematically comparing theimpact of different featurization methods on the performance of ML models forXAS analysis. We evaluated the classification and regression capabilities ofthese models on computed datasets and validated their performance on previouslyunseen experimental datasets. Our analysis revealed an intriguing discovery:the cumulative distribution function (CDF) feature achieves both highprediction accuracy and exceptional transferability. This remarkably robustperformance can be attributed to its tolerance to horizontal shifts in spectra,which is crucial when validating models using experimental data. While thiswork exclusively focuses on XANES analysis, we anticipate that the methodologypresented here will hold promise as a versatile asset to the broaderspectroscopy community.

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization