  Enhanced sampling methods such as metadynamics and umbrella sampling have
become essential tools for exploring the configuration space of molecules and
materials. At the same time, they have long faced the following dilemma: Since
they are only effective with a small number of collective variables (CVs),
choosing a proper set of CVs becomes critical for their accuracy. In this work,
we show that with some technical improvements, reinforced dynamics (RiD) can be
used to efficiently explore the configuration space and free energy landscapes
with a large number of CVs, thereby alleviating the difficulty associated with
choosing two or three CVs. We illustrate this by studying various
representative and challenging examples. As the first example, we construct the
9-dimensional free energy landscape of the peptoid trimer which has energy
barriers of more than 8 kcal/mol. We then study the folding of the protein
chignolin using 18 CVs. With a small computational budget, we are able to
observe 51 transitions between the folded and unfolded states. Finally, we
propose a new protein structure refinement protocol based on RiD. This new
protocol allows us to efficiently employ more than 100 CVs for exploring the
landscape of protein structures and it gives rise to an overall improvement of
14.6 units over the {initial GDT-HA score}.
