Hwang In Hui, Kelly Shelly D, Chan Maria K Y, Stavitski Eli, Heald Steve M, Han Sang Wook, Schwarz Nicholas, Sun Cheng Jun
X-ray Science Division, Argonne National Laboratory, Lemont, IL 60439, USA.
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA.
J Synchrotron Radiat. 2023 Sep 1;30(Pt 5):923-933. doi: 10.1107/S1600577523005684. Epub 2023 Aug 1.
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.
同步加速器数据的处理和分析可能是一项复杂的任务，需要专业的专业知识和技能。我们之前的工作通过使用无监督机器学习开发一个独立的应用程序，解决了X射线发射光谱（XES）数据处理的挑战。然而，分析处理后的光谱的任务仍然是另一个挑战。尽管已知3d过渡金属的非共振Kβ XES能提供诸如氧化态和自旋态等电子结构信息，但找到合适的参数来匹配实验数据是一个耗时且费力的过程。在此，展示了一种基于遗传算法的新XES数据分析方法，并将其应用于锰、钴和镍的氧化物。这种方法也被实现为一个独立的应用程序，即阿贡X射线发射分析2（AXEAP2），它能在最少干预的情况下找到一组能使实验光谱得到高质量拟合的参数。AXEAP2能够找到一组能重现实验光谱的参数，并能深入了解3d电子自旋态、3d - 3p电子交换力和Kβ发射内壳层空穴寿命。
