  Parameterization of interatomic forcefields is a necessary first step in
performing molecular dynamics simulations. This is a non-trivial global
optimization problem involving quantification of multiple empirical variables
against one or more properties. We present EZFF, a lightweight Python library
for parameterization of several types of interatomic forcefields implemented in
several molecular dynamics engines against multiple objectives using
genetic-algorithm-based global optimization methods. The EZFF scheme provides
unique functionality such as the parameterization of hybrid forcefields
composed of multiple forcefield interactions as well as built-in quantification
of uncertainty in forcefield parameters and can be easily extended to other
forcefield functional forms as well as MD engines.
