  Replica exchange (RE) is one of the most popular enhanced-sampling
simulations technique in use today. Despite widespread successes, RE
simulations can sometimes fail to converge in practical amounts of time, e.g.,
when sampling around phase transitions, or when a few hard-to-find
configurations dominate the statistical averages. We introduce a generalized RE
scheme, density-of-states-informed RE (g-RE), that addresses some of these
challenges. The key feature of our approach is to inform the simulation with
readily available, but commonly unused, information on the the density of
states of the system as the RE simulation proceeds. This enables two
improvements, namely, the introduction of resampling moves that actively move
the system towards equilibrium, and the continual adaptation of the optimal
temperature set. As a consequence of these two innovations, we show that the
configuration flow in temperature space is optimized and that the overall
convergence of RE simulations can be dramatically accelerated.
