Emulator-based Decomposition for Structural Sensitivity of Core-level Spectra

TL;DR

This paper introduces an emulator-based decomposition method to analyze how core-level spectra respond to structural changes, enabling identification of key structural factors affecting spectral features.

Contribution

The study presents a novel machine-learning emulator approach for decomposing spectral sensitivity to structural parameters, outperforming traditional partial least squares fitting.

Findings

The method effectively recovers spectral variance linked to structural changes.

It identifies dominant structural degrees of freedom influencing spectra.

The approach aligns well with spectral sensitivity metrics.

Abstract

We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least squares fitting and the observed behavior is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.