atesa 1.0.2

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A Python program for automating transition path sampling with aimless shooting, suitable for experts and novices alike.

Full documentation available here. ATESA has been published in the Journal of Chemical Theory and Computation, here. Please cite this paper in any work making use of ATESA.

ATESA automates a particular Transition Path Sampling (TPS) workflow that uses the flexible-length aimless shooting algorithm of Mullen et al. 2015. ATESA interacts directly with a batch system or job manager to dynamically submit, track, and interpret various simulation and analysis jobs based on one or more initial structures provided to it. The flexible-length implementation periodically checks simulations for commitment to user-defined reactant and product states in order to maximize the acceptance ratio and minimize wasted computational resources.

ATESA implements automation for obtaining a suitable initial transition state, flexible-length aimless shooting, inertial likelihood maximization, committor analysis, umbrella sampling (and analysis with the Multistate Bennett Acceptance Ratio), and equilibrium path sampling. These components constitute a near-complete automation of the workflow between identifying the reaction of interest, and obtaining, validating, and analyzing the energy profile along an unbiased and bona fide reaction coordinate that describes it.

At present, ATESA only supports simulations with Amber and CP2K, and TORQUE/PBS or Slurm batch schedulers. If you are interested in using ATESA with another simulation engine or batch scheduler, please raise an "enhancement" issue describing your needs.

Copyright

Copyright © 2022, Tucker Burgin

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.

Special thanks to Samuel Ellis and the Molecular Sciences Software Institute (MolSSI).