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The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence.
- Source :
-
Journal of Synchrotron Radiation . Sep2023, Vol. 30 Issue 5, p923-933. 11p. - Publication Year :
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09090495
- Volume :
- 30
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Synchrotron Radiation
- Publication Type :
- Academic Journal
- Accession number :
- 172859752
- Full Text :
- https://doi.org/10.1107/S1600577523005684