This work introduces ParAMS—a versatile Python package that aims to make parametrization 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.
Abstract
