  We discuss the rejection-free event-chain Monte-Carlo algorithm and several
applications to dense soft matter systems. Event-chain Monte-Carlo is an
alternative to standard local Markov-chain Monte-Carlo schemes, which are based
on detailed balance, for example the well-known Metropolis-Hastings algorithm.
Event-chain Monte-Carlo is a Markov chain Monte-Carlo scheme that uses
so-called lifting moves to achieve global balance without rejections (maximal
global balance). It has been originally developed for hard sphere systems but
is applicable to many soft matter systems and particularly suited for dense
soft matter systems with hard core interactions, where it gives significant
performance gains compared to a local Monte-Carlo simulation. The algorithm can
be generalized to deal with soft interactions and with three-particle
interactions, as they naturally arise, for example, in bead-spring models of
polymers with bending rigidity. We present results for polymer melts, where the
event-chain algorithm can be used for an efficient initialization. We then move
on to large systems of semiflexible polymers that form bundles by attractive
interactions and can serve as model systems for actin filaments in the
cytoskeleton. The event chain algorithm shows that these systems form networks
of bundles which coarsen similar to a foam. Finally, we present results on
liquid crystal systems, where the event-chain algorithm can equilibrate large
systems containing additional colloidal disks very efficiently, which reveals
the parallel chaining of disks.
