Transition State Ensemble in OpenPathSampling

Software Technical Information

The information in this section describes OpenPathSampling as a whole. Information specific to the additions in this module are in subsequent sections.

Language
Python (3.7)
Documentation Tool
Sphinx, numpydoc format (ReST)
Application Documentation
http://openpathsampling.org
Relevant Training Material
http://openpathsampling.org/latest/examples/
Licence
LGPL, v. 2.1 or later

Authors: Sander Roet

This module is an addition to OpenPathSampling to calculate the snapshots that correspond to the transition state ensemble from a list of trajectories.

Purpose of Module

Often in transition path sampling we want to get an idea about the features of the transition. This is done by generating an ensemble of snapshots that correspond to a committor of approximately 50%. This ensemble gives information about the transition state and the shape of the barrier. This code provides a straightforward way of calculating this ensemble for a given list of trajectories.

This module tries to efficiently find a single transition state frame from each trajectory. This is done by bisection of the trajectory, depending on the current committor. For example, if the current committor is to high (to much ends up in state B) the next index is selected halfway towards the left edge and the current index is set as the new right edge. This is repeated until a committor within a given range is reached or no new frame can be selected.

In the end this module returns a dictionary of shape {snapshot: comittor value} which then can be used for analysis.

The implementation in this module includes:

  • A TransitionStateEnsemble subclass of PathSimulator to run the transition state ensemble simulation.

Background Information

This module builds on OpenPathSampling, a Python package for path sampling simulations. To learn more about OpenPathSampling, you might be interested in reading:

Testing

To test this module you need to download the source files package (see the Source Code section below) and install it using pip install -e . from the root directory of the package. In the ops_tse/tests folder type nosetests test_ops_tse.py to test the module using the nose package.

Examples

  • An IPython 2-D toy example can be found in the examples directory of the the source files (see the Source Code section below). Open it using jupyter notebook simple_tse_example.ipynb (see Jupyter notebook documentation at http://jupyter.org/ for more details)