The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence.
In Hui HwangShelly D KellyMaria K Y ChanEli StavitskiSteve M HealdSang Wook HanNicholas SchwarzCheng Jun SunPublished in: Journal of synchrotron radiation (2023)
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.
Keyphrases
- machine learning
- artificial intelligence
- big data
- data analysis
- density functional theory
- deep learning
- high resolution
- electronic health record
- electron microscopy
- dual energy
- single molecule
- room temperature
- randomized controlled trial
- transition metal
- healthcare
- solid state
- magnetic resonance imaging
- palliative care
- gene expression
- copy number
- genome wide
- heavy metals
- mass spectrometry
- drinking water
- energy transfer