The framework offers a powerful, flexible route to estimating large data sets of physical properties as well as their first derivatives with respect to the force field parameters used in the estimations. This readily allows for the training of such parameters against the physical property data without requiring human intervention at each training epoch through integration with the ForceBalance optimization package. Moreover, the framework's ability to automatically employ reweighting of cached simulations is designed to enable a speed up of successive optimization epochs provided the changes in parameters are sufficiently small. We demonstrate these abilities here by retraining the non-bonded van der Waals (vdW) parameters of the OpenFF 1.0.0 (openff-1.0.0) force field against a total of 114 liquid density and enthalpy of vaporization measurements made at ambient conditions for a set of alcohols, acids, esters, ethers, ketones and alkanes.