posted on 2022-12-15, 14:38authored byTucker Burgin, Samuel Ellis, Heather B. Mayes
Transition path sampling methods are powerful tools for
studying
the dynamics of rare events in molecular simulations. However, these
methods are generally restricted to experts with the knowledge and
resources to properly set up and analyze the often hundreds of thousands
of simulations that constitute a complete study. Aimless Transition
Ensemble Sampling and Analysis (ATESA) is a new open-source software
program written in Python that automates a full transition path sampling
workflow based on the aimless shooting algorithm, streamlining the
process and reducing the barrier to use for researchers new to this
approach. This introduction to ATESA includes a demonstration of a
complete transition path sampling process flow for an example reaction,
including finding an initial transition state, sampling with aimless
shooting, building a reaction coordinate with inertial likelihood
maximization, verifying that coordinate with committor analysis, and
measuring the reaction energy profile with umbrella sampling. We also
describe our implementation of a termination criterion for aimless
shooting based on the Godambe information calculated during model
building with likelihood maximization as well as a novel approach
to constraining simulations to the desired rare event pathway during
umbrella sampling.