Due to the desire to rapidly characterize spectra for arbitrary local environments, datadriven methods for XAS are now enjoying great interest across various communities ; for a general overview, we recommend a review by Timoshenko and Frenkel . These methods attempt to exploit all of the information contained within a spectrum, as opposed to the subset which a heuristic describes, and are enabled by the high availability of theoretical data and the promise of high-throughput experimental XAS data. ML models have been used to automate the analysis of experimental XANES and EXAFS data to gain insights FIG. . Visual description of our workflow. The materials that we study consist of 3d transition metal oxide structures drawn from the Materials Project (MP) database and Open Quantum Materials Database (OQMD) . The inputs to our ML models are XANES spectra computed using FEFF 9 , either downloaded from the MP or computed using the same set of parameters.