A Multicanonical Monte Carlo Ensemble Growth method

In this work, we extend the chain-growth algorithm by T. Garel and H. Orland (J. Phys A, 23.12, L621, 1990) to the multicanonical ensemble. The method belongs to the general class of Population Monte Carlo algorithms, were multiple copies of a statistical system are considered in parallel. Such a stochastic sampling differs from more traditional approaches where one copy of the statistical system is considered at a time. This method produces the density of states of a statistical system, which can be used to produce canonical ensemble distributions and averages by standard re-weighting techniques. It is complementary to powerful and popular Monte Carlo growth methods such as the pruned-enriched Rosenbluth method (PERM), or its multicanonical extension (MuCa PERM), or its flat histogram version (FlatPERM). We discuss its implementation on simple statistical systems, such as the single-chain or multiple-chain growth problems, and its application to the case of polymers adsorbed onto the surface of a protein.