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ATESA: An Automated Aimless Shooting Workflow.

Tucker Burgin1, Samuel Ellis2,3, Heather B Mayes1

  • 1Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States.

Journal of Chemical Theory and Computation
|December 15, 2022
PubMed
Summary
This summary is machine-generated.

Aimless Transition Ensemble Sampling and Analysis (ATESA) simplifies rare event molecular dynamics simulations. This open-source software automates transition path sampling, making complex analyses accessible to more researchers.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics
  • Statistical Mechanics

Background:

  • Transition path sampling (TPS) is crucial for rare event dynamics in molecular simulations.
  • Existing TPS methods require significant expertise and computational resources, limiting accessibility.

Purpose of the Study:

  • Introduce Aimless Transition Ensemble Sampling and Analysis (ATESA), an open-source Python software.
  • Automate and streamline the entire transition path sampling workflow.
  • Lower the barrier to entry for researchers new to TPS.

Main Methods:

  • Utilize the aimless shooting algorithm for automated sampling.
  • Employ inertial likelihood maximization for reaction coordinate construction.
  • Incorporate committor analysis for verification.
  • Apply umbrella sampling for reaction energy profiling.
  • Implement a Godambe information-based termination criterion for aimless shooting.

Main Results:

  • Demonstrate a complete TPS workflow for an example reaction.
  • Showcase ATESA's ability to automate complex simulation processes.
  • Introduce novel methods for simulation control and analysis.

Conclusions:

  • ATESA significantly simplifies and democratizes the use of transition path sampling.
  • The software facilitates efficient rare event dynamics studies in molecular simulations.
  • ATESA empowers researchers with advanced computational tools for chemical dynamics.