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Updated: Jan 23, 2026

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
Published on: April 17, 2018
Modeling L2,3-edge X-ray absorption spectroscopy with linear response exact two-component relativistic time-dependent
Torin F Stetina1, Joseph M Kasper1, Xiaosong Li1
1Department of Chemistry, University of Washington, Seattle, Washington 98195, USA.
This study introduces a new computational method for modeling L-edge X-ray absorption spectra (XAS) using relativistic time-dependent density functional theory (TDDFT). The findings offer improved accuracy for understanding chemical processes via XAS analysis.
Area of Science:
- Computational Chemistry
- Spectroscopy
- Quantum Mechanics
Background:
- X-ray absorption spectroscopy (XAS) provides crucial element-specific chemical insights.
- Relativistic time-dependent density functional theory (TDDFT) has been used for L-edge XAS, but basis set and functional choices require further study.
Purpose of the Study:
- To implement and assess a generalized preconditioned locally harmonic residual algorithm for relativistic TDDFT.
- To comprehensively investigate basis set and density functional kernel effects on L-edge XAS modeling.
- To analyze L2,3-edge spectra of molecular complexes and compare with experimental data.
Main Methods:
- Implementation of a novel algorithm for complex-valued relativistic TDDFT.
- Application of relativistic linear response TDDFT with hybrid iterative diagonalization.
- Systematic error analysis focusing on energetics, intensities, and L2-L3 splitting.
Main Results:
- The developed method accurately models L-edge XAS, providing insights into energetics and intensities.
- Comparison with experimental data shows good agreement for L2,3-edge spectra.
- Evaluation of different theoretical methods and multideterminantal effects was performed.
Conclusions:
- The implemented relativistic TDDFT approach is a robust tool for L-edge XAS.
- This work establishes a foundation for more accurate theoretical predictions in XAS.
- The study highlights the importance of computational method selection for reliable spectral analysis.
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