Determining electronic properties from L-edge X-ray absorption spectra of transition metal compounds with artificial neural networks
Johann Lueder

TL;DR
This paper introduces neural networks trained on simulated spectra to extract electronic and atomic properties of transition metal compounds from L-edge X-ray absorption spectra, overcoming spectral complexity.
Contribution
The study develops adaptable neural networks capable of directly interpreting complex 2p XAS spectra to determine electronic structure and properties of transition metal compounds.
Findings
Neural networks accurately predict electronic configurations from spectra.
Method effectively handles noise and experimental variations.
Validated on experimental spectra of diverse transition metal compounds.
Abstract
X-ray absorption spectroscopy at the L-edge probes transitions of 2p-electrons into unoccupied d-states. Applied to transition metal atoms, this experimental technique can provide valuable information about the electronic structure of d-states. However, multiplet effects, spin-orbit coupling, a large number of possible transitions can cause a rather involved nature of 2p XAS spectra, which can often complicate extracting of information directly from them. Here, artificial neural networks trained on simulated spectra of a 2p XAS model Hamiltonian are presented that can directly determine information about atomic properties and the electronic configuration of d-states from L-edge X-ray absorption spectra. Moreover, the adaptable nature of artificial neural networks (ANNs) allows extending their capability to obtain information about the electronic ground state and core hole lifetimes from…
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