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.

Data set includes 10000 snapshots of MD trajectories and calculated XES, XAS, XPS spectra, and also codes for the analysis (search for the best emulator, ECA analysis and PLSSVD analysis), and figure generation scripts.

The protocol is described in the README file.