  Metadynamics, a member of the `flat histogram' class of advanced sampling
algorithms, has been widely used in molecular simulations to drive the
exploration of states separated by high free energy barriers and promote
comprehensive sampling of free energy landscapes defined on collective
variables (CVs) which characterize the state of the system. Typically, the
methods encounter severe limitations when exploring large numbers of CVs. A
recently proposed variant, parallel bias metadynamics (PBMetaD), promises to
aid in exploring free energy landscapes along with multiple important
collective variables by exchanging the $n$-dimensional free energy landscape
required by standard methods for $n$ one-dimensional marginal free energy
landscapes. In this study, we systematically examine how parallel biasing
affects the convergence of free energy landscapes along with each variable
relative to standard methods and the effectiveness of the parallel biasing
strategy for addressing common bottlenecks in the use of advanced sampling to
calculate free energies.
