  A common task is the determination of system parameters from spectroscopy,
where one compares the experimental spectrum with calculated spectra, that
depend on the desired parameters. Here we discuss an approach based on a
machine learning technique, where the parameters for the numerical calculations
are chosen from Gaussian Process Regression (GPR). This approach does not only
quickly converge to an optimal parameter set, but in addition provides
information about the complete parameter space, which allows for example to
identify extended parameter regions where numerical spectra are consistent with
the experimental one. We consider as example dimers of organic molecules and
aim at extracting in particular the interaction between the monomers, and their
mutual orientation. We find that indeed the GPR gives reliable results which
are in agreement with direct calculations of these parameters using quantum
chemical methods.
