Multi-modal characterization techniques will dramatically accelerate materials research and discovery; however, this development will result in the generation of significant quantities of data. Specifically, the advanced concept of simultaneous multimodal X-ray microprobe measurements (X-ray fluorescence, emission, and diffraction) for APS-U enhancement beamline 25-ID, will achieve full structural, chemical, and compositional information in an X-ray image with micron resolution over cm length scales.
We apply artificial Intelligence methods to tackle the interpretation of experimental spectra with a goal of achieving real-time data interpretation and experiment steering capabilities. Our team has demonstrated the use of advanced X-ray emission spectroscopy (XES) for simultaneous non-resonant X-ray emission at multiple edges and elements [1]. Advanced materials understanding from XES allow spin, ligand, oxidation state of multiple elements simultaneously at subsecond time scales to understand advanced materials with multiple coordinated response to external stimuli.
To steer experiments a user-friendly software is available and applies unsupervised machine learning to process XES data from detector signals [2]. Using simulated data from charge transfer multiplet theory, we have also used genetic algorithm to determine charge, spin, and electronic structure properties from XES data of transition metal [3]. Initially funded as an LDRD, the project is currently funded by DOE as a collaboration between Argonne National Laboratory, Brookhaven National Laboratory, and Lawrence Berkeley National Laboratory, and APS operation fudned by DOE.
Summary of project results: Demonstrated advanced XES spectrometer for simultaneous non-resonant X-ray emission at multiple edges/elements with large sample environment for in-situ and simultaneous XRF measurements. [1] Software package to process X-ray emission spectroscopy (XES) with unsupervised machine learning. [2] Method to analyze XES data with genetic algorithm artificial Intelligence. [3]
| Project Contacts
Chengjun Sun
Mikhail A. Solovyev
Shelly Diane Kelly
Maria K. Chan
Xiaoyi Zhang
Nicholas Schwarz
More Information
• Advanced X-Ray Emission Spectrometers
• AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning
• The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence |
