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 inJournal of synchrotron radiation Vol. 30; no. 5; pp. 923 - 933
Main Authors Hwang, In-Hui, Kelly, Shelly D., Chan, Maria K. Y., Stavitski, Eli, Heald, Steve M., Han, Sang-Wook, Schwarz, Nicholas, Sun, Cheng-Jun
Format Journal Article
LanguageEnglish
Published United States John Wiley & Sons, Inc 01.09.2023
International Union of Crystallography (IUCr)
International Union of Crystallography
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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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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