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The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence
- Source :
- Journal of Synchrotron Radiation, Vol 30, Iss 5, Pp 923-933 (2023)
- Publication Year :
- 2023
- Publisher :
- International Union of Crystallography, 2023.
-
Abstract
- The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kβ XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d–3p electron exchange force and Kβ emission core-hole lifetime.
Details
- Language :
- English
- ISSN :
- 16005775
- Volume :
- 30
- Issue :
- 5
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Synchrotron Radiation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.10d62d53e16e43308204f8ea683608bd
- Document Type :
- article
- Full Text :
- https://doi.org/10.1107/S1600577523005684