AI supported X-ray absorption spectroscopy at the L-edge: solving the inverse and direct problem

Johann Lueder^1*

¹Department of Materials and Optoelectronic Science, National Sun Yat-Sen University, Kaohsiung, Taiwan

* Presenter:Johann Lueder, email:johann.lueder@gmail.com

X-ray absorption spectroscopy (XAS) is an element- and state-selective technique, which can reveal detailed information on the nature of d-electrons when applied to the L-edge of transition metals. For this excitation process, a large number of possible transitions obeying dipole selection rules, multiplets, spin-orbit coupling (SOC), temperature and lifetime effects, as well as the dependence on the chemical surrounding of the metal ion, can result in complicated and feature-rich spectra. Often, a manual trail-and-error fitting procedure reproducing an experimental spectrum with a model Hamiltonian is used to elucidate electronic states and effects. This can be labour intensive and time-consuming because the employed model Hamiltonian can have, in some cases, ten or more parameters that must be explored simultaneously.
Here, artificial neural network-based approaches are presented that can reliably determine a set of most suitable parameters describing the electronic state of the metal ion and its surrounding from an experimental spectrum as well as predict L-edge spectra with high accuracy. The importance of different network architectures, training sets and applications conditions is discussed. Moreover, the effect of background signals and noise is discussed as well as how to include temperature and experimental broadening conditions into the method.

Keywords: L-edge, machine learning, X-ray spectroscopy, deep learning