  We present a method for enhanced sampling of molecular dynamics simulations
using stochastic resetting. Various phenomena, ranging from crystal nucleation
to protein folding, occur on timescales that are unreachable in standard
simulations. This is often caused by broad transition time distributions in
which extremely slow events have a non-negligible probability. Stochastic
resetting, i.e., restarting simulations at random times, was recently shown to
significantly expedite processes that follow such distributions. Here, we
employ resetting for enhanced sampling of molecular simulations for the first
time. We show that it accelerates long-timescale processes by up to an order of
magnitude in examples ranging from simple models to molecular systems. Most
importantly, we recover the mean transition time without resetting - typically
too long to be sampled directly - from accelerated simulations at a single
restart rate. Stochastic resetting can be used as a standalone method or
combined with other sampling algorithms to further accelerate simulations.
