OpenPathSampling: A Python framework for path sampling simulations. I. Basics

Abstract

Transition path sampling techniques allow molecular dynamics simulations of complex systems to focuson rare dynamical events, providing insight into mechanisms and the ability to calculate rates inaccessibleby ordinary dynamics simulations. While path sampling algorithms are conceptually as simple as importancesampling Monte Carlo, the technical complexity of their implementation has kept these techniquesout of reach of the broad community. Here, we introduce an easy-to-use Python framework called Open-PathSampling (OPS) that facilitates path sampling for (bio)molecular systems with minimal effort and yetis still extensible. Interfaces to OpenMM and an internal dynamics engine for simple models are providedin the initial release, but new molecular simulation packages can easily be added. Multiple ready-to-usetransition path sampling methodologies are implemented, including standard transition path sampling (TPS)between reactant and product states, transition interface sampling (TIS) and its replica exchange variant(RETIS), as well as recent multistate and multiset extensions of transition interface sampling (MSTIS, MISTIS).In addition, tools are provided to facilitate the implementation of new path sampling schemes built on basicpath sampling components. In this paper, we give an overview of the design of this framework and illustratethe simplicity of applying the available path sampling algorithms to a variety of benchmark problems.