The AXEAP2 program for K β X-ray emission spectra analysis using artificial intelligence
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 ta...
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Published in | Journal of synchrotron radiation Vol. 30; no. 5; pp. 923 - 933 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
John Wiley & Sons, Inc
01.09.2023
International Union of Crystallography (IUCr) International Union of Crystallography |
Subjects | |
Online Access | Get full text |
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Summary: | 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
K
β XES of 3
d
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 3
d
electron spin state, 3
d
–3
p
electron exchange force and
K
β emission core-hole lifetime. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF) BNL-224958-2023-JAAM AC02-06CH11357; PRJ1007376; SC0012704; KC040602; 35909 USDOE Laboratory Directed Research and Development (LDRD) Program |
ISSN: | 1600-5775 0909-0495 1600-5775 |
DOI: | 10.1107/S1600577523005684 |