X-ray absorption near edge structure (XANES) spectroscopy is a powerful
technique for characterizing the chemical state and symmetry of individual
elements within materials, but requires collecting data at many energy points
which can be time-consuming. While adaptive sampling methods exist for
efficiently collecting spectroscopic data, they often lack domain-specific
knowledge about XANES spectra structure. Here we demonstrate a
knowledge-injected Bayesian optimization approach for adaptive XANES data
collection that incorporates understanding of spectral features like absorption
edges and pre-edge peaks. We show this method accurately reconstructs the
absorption edge of XANES spectra using only 15-20% of the measurement points
typically needed for conventional sampling, while maintaining the ability to
determine the x-ray energy of the sharp peak after absorption edge with errors
less than 0.03 eV, the absorption edge with errors less than 0.1 eV; E
overall root-mean-square errors less than 0.005 compared to compared to
traditionally sampled spectra. Our experiments on battery materials and
catalysts demonstrate the method’s effectiveness for both static and dynamic
XANES measurements, improving data collection efficiency and enabling better
time resolution for tracking chemical changes. This approach advances the
degree of automation in XANES experiments reducing the common errors of under-
or over-sampling points in near the absorption edge and enabling dynamic
experiments that require high temporal resolution or limited measurement time.

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