  Quantitatively accurate molecular models, called force fields, are necessary
for predictive molecular simulations. However, optimizing force fields to
accurately reproduce experimental properties is a challenging endeavor. Here we
present a machine learning directed workflow for force field optimization.
Surrogate-assisted optimization is used to evaluate millions of prospective
force field parameters while requiring only a small fraction of those to be
evaluated with molecular simulations. The generality of the approach is
demonstrated with two distinct test cases: optimizing force fields for
simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an
ammonium perchlorate (AP) crystal phase. Our workflow identifies parameter sets
for HFC-32 and HFC-125 which reproduce six experimental VLE properties to
within 1.5$\%$ and 2.5$\%$ error, respectively, which are remarkably better
than existing conventional all-atom force fields. When applied to AP, our
workflow identifies parameter sets that better reproduce the AP structure than
the best available force field in the literature and our own hand-tuned AP
force field. The force field optimization workflow is general and can be used
to improve the accuracy of existing force fields and develop new force fields
for specific systems.
