where D(P||M) is the Kullback-Leibler divergence and 𝑀 = 1 2 (𝑃 + 𝑄). The JS distance between the two histograms was then used as the convergence criterion: if the JS distance was less than or equal to 0.1, roughly the 10 th percentile of similarity between the two histograms, the simulation was terminated. If the JS distance was greater than or equal to 0.1 or the total number of decorrelated samples was less than 50 (see below for explanation), the production simulation was extended for a pre-defined time. This was repeated until convergence, or until either of the following two scenarios were met: a total simulation time of 6.5 ns had been achieved and more than 50 decorrelated samples were acquired in total, or a total simulation time of 10.5 ns had been achieved (Fig. ). frequency of convergence checking is controlled by the additional simulation length hyperparameter. The probability of the gradient time series being observed as converged at any point in the simulation is nonzero. Thus, more frequent convergence testing will result in a higher likelihood of a false positive convergence. The JS distance convergence threshold hyperparameter has a similar effect: higher values increase the likelihood of obtaining faster convergence and false positives because the two histograms need lower degrees of similarity to meet this threshold, while lower values decrease this probability but also may cause unnecessarily longer simulations. We chose to use at least 50 decorrelated samples to be comfortably above the rule of thumb of sample sizes in standard statistical analysis, but not too high as to frequently force additional sampling for marginal benefit as the uncertainty will decrease with the inverse square of the sample size.