Emulator-based decomposition for structural sensitivity of core-level spectra

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.