The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence
| Autor: | In-Hui Hwang, Shelly D. Kelly, Maria K. Y. Chan, Eli Stavitski, Steve M. Heald, Sang-Wook Han, Nicholas Schwarz, Cheng-Jun Sun |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Synchrotron Radiation, Vol 30, Iss 5, Pp 923-933 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1600-5775 16005775 |
| DOI: | 10.1107/S1600577523005684 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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