However, this approach appears robust in practice. The Gellman-Rubin diagnostic is a popular metric used to diagnose convergence in MCMC simulations based on the difference between intra-run and inter-run variances. However, we found that the tendency of the uncertainties not to decrease with increasing sampling time (the tendency of systems to remain trapped in local minima) meant that this diagnostic was not useful for determining the termination point of our simulations. Effectively, the convergence of our simulations often did not improve with increasing sampling time, and therefore the degree of convergence was not a useful stopping criterion. We note that a more recent version of the Gelman-Rubin diagnostic may be more useful for this purpose. Still, we found this diagnostic to be very useful for highlighting windows where substantial sampling problems occurred (Section S13).