  Analyzing coordination environments using X-ray absorption spectroscopy has
broad applications ranging from solid-state physics to material chemistry.
Here, we show that random forest models can identify the main coordination
environment from K-edge X-ray absorption near edge structure (XANES) with a
high accuracy of 85.4% and all associated coordination environments with a high
Jaccard score of 81.8% for 33 cation elements in oxides, significantly
outperforming other machine learning (ML) models. In a departure from prior
works, we used a robust description of the coordination environment as a
distribution over 25 distinct coordination motifs with coordination numbers
ranging from 1-12. The random forest models were trained on the world's largest
database of ~190,000 computed K-edge XANES spectra. Furthermore, the random
forest models can be applied to predict the coordination environment from
experimental K-edge XANES with minimal loss in accuracy (82.1%) due to the use
of data augmentation. A drop-out feature importance analysis highlights the key
roles that the pre-edge and main-peak regions play in coordination environment
identification, with the post-peak region becoming increasingly important at
higher coordination numbers. This work provides a general strategy to identify
the coordination environment from K-edge XANES across broad chemistries, paving
the way for future advancements in the application of ML to spectroscopy.
