  Accurate force fields are necessary for predictive molecular simulations.
However, developing force fields that accurately reproduce experimental
properties is challenging. Here, we present a machine learning directed,
multiobjective optimization workflow for force field parameterization that
evaluates millions of prospective force field parameter sets while requiring
only a small fraction of them to be tested with molecular simulations. We
demonstrate the generality of the approach and identify multiple low-error
parameter sets for two distinct test cases: simulations of hydrofluorocarbon
(HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal
phase. We discuss the challenges and implications of our force field
optimization workflow.
