  Calculating free energies is an important and notoriously difficult task for
molecular simulations. The rapid increase in computational power has made it
possible to probe increasingly complex systems, yet extracting accurate free
energies from these simulations remains a major challenge. Fully exploring the
free energy landscape of, say, a biological macromolecule typically requires
sampling large conformational changes and slow transitions. Often, the only
feasible way to study such a system is to simulate it using an enhanced
sampling method. The accelerated weight histogram (AWH) method is a new,
efficient extended ensemble sampling technique which adaptively biases the
simulation to promote exploration of the free energy landscape. The AWH method
uses a probability weight histogram which allows for efficient free energy
updates and results in an easy discretization procedure. A major advantage of
the method is its general formulation, making it a powerful platform for
developing further extensions and analyzing its relation to already existing
methods. Here, we demonstrate its efficiency and general applicability by
calculating the potential of mean force along a reaction coordinate for both a
single dimension and multiple dimensions. We make use of a non-uniform, free
energy dependent target distribution in reaction coordinate space so that
computational efforts are not wasted on physically irrelevant regions. We
present numerical results for molecular dynamics simulations of lithium acetate
in solution and chignolin, a 10-residue long peptide that folds into a
$\beta$-hairpin. We further present practical guidelines for setting up and
running an AWH simulation.
