Zooming across the Free-Energy Landscape: Shaving Barriers, and Flooding Valleys

A robust
importance-sampling algorithm for mapping free-energy
surfaces over geometrical variables, coined meta-eABF, is introduced.
This algorithm shaves the free-energy barriers and floods valleys
by incorporating a history-dependent potential term in the extended
adaptive biasing force (eABF) framework. Numerical applications on
both toy models and nontrivial examples indicate that meta-eABF explores
the free-energy surface significantly faster than either eABF or metadynamics
(MtD) alone, without the need to stratify the reaction pathway. In
some favorable cases, meta-eABF can be as much as five times faster
than other importance-sampling algorithms. Many of the shortcomings
inherent to eABF and MtD, like kinetic trapping in regions of configurational
space already adequately sampled, the requirement of prior knowledge
of the free-energy landscape to set up the simulation, are readily
eliminated in meta-eABF. Meta-eABF, therefore, represents an appealing
solution for a broad range of applications, especially when both eABF
and MtD fail to achieve the desired result.