ATESA: Aimless Transition Ensemble Sampling and Analysis

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

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.

The batch systems and molecular simulations packages currently supported by ATESA (please raise an issue with the “enhancement” label on our GitHub page if you’d like to see something added to this list!):

Batch Systems

• Slurm
• PBS/TORQUE

Simulations Packages

• Amber

For Users:

• Theory and Definitions
  • Why Do Rare Event Sampling?
  • What is Transition Path Sampling?
  • What is Aimless Shooting?
  • When is Aimless Shooting the Right Tool?
  • What is ATESA, and Why Should I Use It?
  • What is Likelihood Maximization?
  • What is Committor Analysis?
  • What is Umbrella Sampling?
  • What is Pathway-Restrained Umbrella Sampling?
  • What is Equilibrium Path Sampling?
• Getting Started with ATESA
  • Installation
  • Usage
  • Setting Up Simulation Files
• Example Study
  • Basic Workflow
  • Initial Setup and the Model
  • Finding a Transition State
  • Aimless Shooting
  • Likelihood Maximization and Reaction Coordinate Evaluation
  • Committor Analysis
  • Umbrella Sampling
  • Conclusion
• The Configuration File
  • Core Settings
  • Full Configuration Options
• Auxiliary Scripts
  • lmax.py: Likelihood Maximization
  • rc_eval.py: Reaction Coordinate Evaluation
  • mbar.py: Energy Profiles from US
  • boltzmann_weight.py: Energy Profiles from EPS
• On Termination Criteria
  • The Sampling Problem
  • Relevance to Aimless Shooting
  • Choosing When to Stop with Committor Analysis
  • Information Error
• Troubleshooting
  • Technical Issues
  • Job Type-Specific Issues

API:

• atesa package

Indices and tables

• Index
• Module Index
• Search Page