Articles about OpenPathSampling published

Path sampling methods are powerful tools for studying rare events. However, they were not as widely-used as they might be, in part because there was no standard software for path sampling.

My colleagues and I wrote OpenPathSampling (OPS), a Python package for path sampling simulations, as well as other trajectory-based approaches to rare events. This was a major undertaking, and in the process, we developed a new formalism to describe the path ensembles used in these methods.

As a result, we wrote two papers describing the code. The first is for general users, and explains how to use our package to do common path sampling tasks. The second is aimed more at methods developers, and explains our new formalism and shows how to use it, and other parts of OPS, to quickly and easily develop new methods.

Both have now been officially published in Journal of Chemical Theory and Computation. The publication of these articles was also the subject of a news item on the E-CAM Centre of Excellence’s website.

David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera, and Peter G. Bolhuis, “OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics” J. Chem. Theory Comput. 15, 813 (2019).

David W. H. Swenson, Jan-Hendrik Prinz, Frank Noe, John D. Chodera, and Peter G. Bolhuis, “OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and Sample Schemes” J. Chem. Theory Comput. 15, 837 (2019).