X-ray absorption near edge structure(XANES)spectroscopy is a powerful technique for characterizing the chemical state andsymmetry of individual elements within materials,but requires collecting data at many energy poi...X-ray absorption near edge structure(XANES)spectroscopy is a powerful technique for characterizing the chemical state andsymmetry 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 the structure of XANES spectra.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 the absorption edge with errors less than 0.03 eV,the absorption edge with errors less than 0.1 eV;and overall root-mean-square errors less than 0.005 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 near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.展开更多
基金supported by the U.S. DOE Office of Science-Basic Energy Sciences, under Contract No. DE-AC02-06CH11357The research was also supported by the Canadian Light Source and its funding partners. Data for demonstrations were collected at beamlines 25-ID, 20-BM, and 10-ID (MRCAT) of the Advanced Photon Source+1 种基金The NMC-111 electrodes were supplied by the U.S. Department of Energy’s (DOE) CAMP (Cell Analysis, Modeling and Prototyping) Facility, Argonne National LaboratoryThe CAMP Facility is fully supported by the DOE Vehicle Technologies Office (VTO).
文摘X-ray absorption near edge structure(XANES)spectroscopy is a powerful technique for characterizing the chemical state andsymmetry 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 the structure of XANES spectra.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 the absorption edge with errors less than 0.03 eV,the absorption edge with errors less than 0.1 eV;and overall root-mean-square errors less than 0.005 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 near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.
