We attribute a number of factors to the improved performance of SMA-MD compared to MD. First, SMA-MD combines data aggregation and post-processing. On the one hand, even if simulations used as training data are not fully converged, it is possible to learn relevant structures from similar molecules. On the other, reweighting and short simulations help to equilibrate the populations and compensate for small deviations in the geometries generated by the surrogate. Combining these two elements, SMA-MD shows robustness against training on biased data (non-converged simulations). Second, SMA-MD does not need initial conditions. One important limitation of MD simulations is their sensitivity to the initial coordinates, often obtained from an experimental crystal structure. Indeed, a main success of Markov state modeling is its ability to use simulation data from different initial conditions to make quantitative predictions. In contrast, SMA-MD not only does not require an initial condition but provides a way of initializing simulations with several different representative initial structures to boost convergence. Third, SMA-MD allows for parallelization. MD simulations are intrinsically sequential, however, all the individual components in SMA-MD allow for parallelization, which makes SMA-MD a more suitable method for modern computing hardware.