A further, and likely the biggest issue, is that the number of properties which may accurately estimated using cached simulation data reweighting is on average less than 50% of the total number of properties to estimate. This is a consequence of the optimizer performing, in a sense, too well, and the force field parameters varying by too large an amount at each new iteration compared to the previous iteration, such that there are an insufficient number of effective samples at the new state. While the step size of the algorithm could be reduced in order to ensure that reweighting is employed more frequently, it is not clear that this would always be optimal. It can be seen in Figure that the objective function has already greatly decreased by the first few iterations before there is even enough data to be able to employ reweighting. It should be noted however that this optimization was performed on a relatively small training set. For large training sets it is likely that the optimization would take longer to converge to a minimum, and hence in these cases it is likely that reducing the step size so that reweighting is employed would be beneficial.