  Enhanced sampling techniques have become an essential tool in computational
chemistry and physics, where they are applied to sample activated processes
that occur on a time scale that is inaccessible to conventional simulations.
Despite their popularity, it is well known that they have constraints that
hinder their applications to complex problems. The core issue lies in the need
to describe the system using a small number of collective variables (CVs). Any
slow degree of freedom that is not properly described by the chosen CVs will
hinder sampling efficiency. However, exploration of configuration space is also
hampered by including variables that are not relevant to describe the activated
process under study. This paper presents the Adaptive Topography of Landscape
for Accelerated Sampling (ATLAS), a new biasing method capable of working with
many CVs. The root idea of ATLAS is to apply a divide-and-conquer strategy
where the high-dimensional CVs space is divided into basins, each of which is
described by an automatically-determined, low-dimensional set of variables. A
well-tempered metadynamics-like bias is constructed as a function of these
local variables. Indicator functions associated with the basins switch on and
off the local biases, so that the sampling is performed on a collection of
low-dimensional CV spaces, that are smoothly combined to generate an
effectively high-dimensional bias. The unbiased Boltzmann distribution is
recovered through reweighing, making the evaluation of conformational and
thermodynamic properties straightforward. The decomposition of the free-energy
landscape in local basins can be updated iteratively as the simulation
discovers new (meta)stable states.
