Ab Initio Calculations of XUV Ground and Excited States for First-Row Transition Metal Oxides

TL;DR

This paper develops an ab initio Bethe-Salpeter equation approach to accurately calculate XUV spectra of first-row transition metal oxides in various states, aiding interpretation of transient X-ray spectroscopic data.

Contribution

It introduces a new theoretical method that incorporates photoexcited state effects into XUV spectral calculations for solid-state materials, enhancing analysis of transient spectroscopic experiments.

Findings

Accurate XUV spectra for multiple transition metal oxides were computed.

Decomposition of core-valence excitons offers physical insight into spectral features.

Method is adaptable to various X-ray edges and states.

Abstract

Transient X-ray spectroscopies have become ubiquitous in studying photoexcited dynamics in solar energy materials due to their sensitivity to carrier occupations and local chemical or structural dynamics. The interpretation of solid-state photoexcited dynamics, however, is complicated by the core-hole perturbation and the resulting many-body dynamics. Here, an ab initio, Bethe-Salpeter equation (BSE) approach is developed that can incorporate photoexcited state effects for solid-state materials. The extreme ultraviolet (XUV) absorption spectra for the ground, photoexcited, and thermally expanded states of first row transition metal oxides - $TiO_2, \alpha-Cr_2O_3, \beta-MnO_2, \alpha-Fe_2O_3, Co_3O_4, NiO, CuO, and ZnO$ - are calculated to demonstrate the accuracy of this approach. The theory is used to decompose the core-valence excitons into the separate components of the X-ray transition Hamiltonian for each of the transition metal oxides investigated. The decomposition provides a physical intuition about the origins of XUV spectral features as well as how the spectra will change following photoexcitation. The method is easily generalized to other K, L, M, and N edges to provide a general approach for analyzing transient X-ray absorption or reflection data.