The training hyperparameters as required by ForceBalance are provided in Table , and are described more fully in . All properties were computed using the default density and enthalpy of vaporization schemas but the number of molecules included in the simulation box when performing the simulations was reduced from 1000 to 500. This was done to increase the likelihood that the cached data reweighting would be employed when estimating the physical properties, given that the degree of overlap between two states decreases as the system size increases. By default only the four most recent pieces of cached simulation data are chosen for reweighting. This limits the overhead associated with attempting to reweight data which does not sufficiently overlap with the current state, which if uncapped would increase linearly with the number of training iterations performed. This fluctuation is observed due to noise in the estimated physical properties and hence also in their first derivatives with respect to the force field parameters being trained. The reweighting of cached simulation data therefore enables a sufficiently comparable estimation of both the objective function and its derivative with respect to the force field parameters being trained to be used as part of the parameter training as an appropriate replacement to the full simulation approach. The cumulative time taken to reach the end of each training iteration is also shown in Figure . While hypothesized, based on previous use of reweighting in Bayesian inference of parameters , that employing reweighting of cached simulation data should enable a large speed up once enough data has been stored