Towards structural reconstruction from X-ray spectra.

Area of Science:

• Materials Science
• Computational Chemistry
• Spectroscopy

Background:

• X-ray emission spectroscopy (XES) provides insights into material electronic structure.
• Analyzing XES spectra, especially under extreme conditions like high pressure, is complex.
• Simulations are crucial for understanding spectral features but require efficient analysis methods.

Purpose of the Study:

• To develop a machine learning approach for predicting statistical moments of Ge K-edge X-ray emission spectra.
• To establish an inverse mapping from spectral moments to structural descriptors.
• To quantitatively analyze pressure-induced structural changes in amorphous GeO2 using spectral data.

Main Methods:

• Simulated Ge K-edge X-ray emission spectra for amorphous GeO2 at elevated pressures.
• Machine learning models trained on Coulomb matrix descriptors and spectral moments.
• Spectral-significance-guided dimensionality reduction for inverse mapping.
• Emulator-based component analysis for filtering structural information.

Main Results:

• Machine learning reliably predicts Kβ'' and Kβ2 peak moments from Coulomb matrix descriptors.
• An approximate inverse mapping from spectral moments to pseudo-Coulomb matrices was constructed.
• The method accurately reproduces ensemble-mean distances and pressure-induced coordination changes in amorphous GeO2.

Conclusions:

• Machine learning offers a robust tool for analyzing complex X-ray emission spectra.
• The developed approach enables quantitative analysis of structural changes solely from spectral data.
• This method effectively filters simulation artifacts, providing reliable structural insights.