  This work introduces ParAMS -- a versatile Python package that aims to make
parameterization workflows in computational chemistry and physics more
accessible, transparent and reproducible. We demonstrate how ParAMS facilitates
the parameter optimization for potential energy surface (PES) models, which can
otherwise be a tedious specialist task. Because of the package's modular
structure, various functionality can be easily combined to implement a
diversity of parameter optimization protocols. For example, the choice of PES
model and the parameter optimization algorithm can be selected independently.
An illustration of ParAMS' strengths is provided in two case studies: i) a
density functional-based tight binding (DFTB) repulsive potential for the
inorganic ionic crystal ZnO, and ii) a ReaxFF force field for the simulation of
organic disulfides.
