multicanonical
Multicanonical methods are Monte Carlo algorithms designed to overcome sampling barriers in systems with rugged energy landscapes. The central idea is to sample configurations with a weight that yields a nearly flat probability distribution of energy, thereby enabling transitions that are rare in canonical simulations.
Concretely, one adjusts a weight function W(E) to mimic the inverse density of states, such that the
MUCA originated in lattice statistical mechanics through the work of Berg and Neuhaus in the early 1990s
Applications include spin models, polymers, and biomolecules, where MUCA helps to study phase transitions, folding pathways,
Challenges include determining accurate weight factors, ensuring convergence, and controlling statistical errors in reweighting, especially at
Related concepts include the density of states g(E) and entropy S(E); MUCA data can be used to