When the NNs are used over longer time-horizons to mix independently, each NN is able to competently extrapolate from the training initial conditions to mixed configurations and mix more than 80% of validation configurations to their final mixed states with respect to number of segments [Fig. ]. Those NNs with denser training datasets perform slightly better on average than those with sparser training datasets. However, there is significant variability in performance between individual realizations of the NN with respect to mixing even when the variability in MSE loss is small [Fig. ].