Abstract:
The Kβ X-ray emission spectrum (XES) of transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, which is crucial for understanding their spin and oxidation states (Glatzel & Bergmann, 2005). The spectrum is commonly treated using ligand-field multiplet theory (de Groot & Kotani, 2008), a semi-empirical theory that uses parameters to explicitly account for many of the effects present in XES. However, determining the values of these parameters remains a challenge (Huang et al., 2022). We present a new methodology that applies Bayesian optimization to ligand-field theory to determine parameter values. The algorithm is tested on a collection of Mn, Co, and Ni oxides. We demonstrate significantly improved accuracy and provide visualizations of parameter impacts on spectral shape; we are able to find optimal values for up to four parameters and analyze the individual impact of each parameter on both the spectral shape and how certain parameters might be dependent on one another. This advancement will enhance our understanding of transition metal properties, facilitating applications across various scientific fields.

References:

1. de Groot, F., & Kotani, A. (2008). Core Level Spectroscopy of Solids. Taylor and Francis Group.
2. Glatzel, P., & Bergmann, U. (2005). High-resolution 1s core hole X-ray spectroscopy in 3d transition metal complexes—electronic and structural information. Coordination Chemistry Reviews, 249(1-2), 65-95.
3. Huang, I. et al., (2022). The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence. Journal of Synchrotron Radiation, 29(5), 923-934.