  Molecular dynamics (MD) simulations are useful in obtaining thermodynamic and
kinetic properties of bio-molecules but are limited by the timescale barrier,
i.e., we may be unable to efficiently obtain properties because we need to run
microseconds or longer simulations using femtoseconds time steps. While there
are several existing methods to overcome this timescale barrier and efficiently
sample thermodynamic and/or kinetic properties, problems remain in regard to
being able to sample unknown systems, deal with high-dimensional space of
collective variables, and focus the computational effort on slow timescales.
Hence, a new sampling method, called the "Concurrent Adaptive Sampling (CAS)
algorithm," has been developed to tackle these three issues and efficiently
obtain conformations and pathways. The method is not constrained to use only
one or two collective variables, unlike most reaction coordinate-dependent
methods. Instead, it can use a large number of collective variables and uses
macrostates (a partition of the collective variable space) to enhance the
sampling. The exploration is done by running a large number of short
simulations, and a clustering technique is used to accelerate the sampling. In
this paper, we introduce the new methodology and show results from
two-dimensional models and bio-molecules, such as penta-alanine and triazine
polymer.
