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Analyzing coordination environments using X-ray absorption spectroscopy hasbroad applications ranging from solid-state physics to material chemistry.Here, we show that random forest models can identify the main coordinationenvironment from K-edge X-ray absorption near edge structure (XANES) with ahigh accuracy of 85.4% and all associated coordination environments with a highJaccard score of 81.8% for 33 cation elements in oxides, significantlyoutperforming other machine learning (ML) models. In a departure from priorworks, we used a robust description of the coordination environment as adistribution over 25 distinct coordination motifs with coordination numbersranging from 1-12. The random forest models were trained on the world's largestdatabase of ~190,000 computed K-edge XANES spectra. Furthermore, the randomforest models can be applied to predict the coordination environment fromexperimental K-edge XANES with minimal loss in accuracy (82.1%) due to the useof data augmentation. A drop-out feature importance analysis highlights the keyroles that the pre-edge and main-peak regions play in coordination environmentidentification, with the post-peak region becoming increasingly important athigher coordination numbers. This work provides a general strategy to identifythe coordination environment from K-edge XANES across broad chemistries, pavingthe way for future advancements in the application of ML to spectroscopy.

Random Forest Models for Accurate Identification of Coordination Environments from X-ray Absorption Near-Edge Structure