Exploring the Potential of Parallel-Biasing in Flat Histogram Methods

Metadynamics, a member fo the "flat histogram" class of advanced sampling algorithms, has been widely used in molecular simulations to drive exploration of states that are separated by high free energy barriers and promote the sampling of full free energy landscapes. A recently proposed variant, paralled bias metadynamics (PBMetaD) promises to aid in exploration of free energy landscape s along 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 convergence of free energy landscapes along 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.