Efficiently Sampling Conformations and Pathways Using the Concurrent Adaptive Sampling (CAS) Algorithm

Abstract

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.