Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning
Source abstract
Abstract
Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about distributions and locations of atoms, their coordination numbers and oxidation states, and the bonding characteristics [1]. However, analysis of XAS/EELS data often relies on matching the spectra of an unknown experimental sample to a series of simulated or experimental spectra of standard samples. Here, this limits analysis throughput and the ability to extract quantitative information from a sample.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · X-ray Diffraction in Crystallography · Computational Drug Discovery Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory , Berkeley, CA , United States
Department of Chemistry, University of California , Berkeley, CA , United States
Computational Science Initiative, Brookhaven National Laboratory , Upton, NY , United States
Center for Functional Nanomaterials, Brookhaven National Laboratory , Upton, NY , United States
References (6)
Show source-provided references
1. Yasuda
2. Gleason
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