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Computing transition path theory quantities with trajectory stratification.

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Transition path theory statistics can now be efficiently computed from complex trajectory data. Simple data structures overcome challenges in identifying reactive trajectories, enhancing sampling methods.

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Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Chemical kinetics

Background:

  • Transition path theory (TPT) analyzes reactive trajectories to compute chemical reaction statistics.
  • Enhanced sampling methods often use trajectory branching and pruning, creating challenges for TPT analysis.
  • Identifying reactive trajectory segments in such data requires complex temporal analysis.

Purpose of the Study:

  • To develop an efficient method for applying transition path theory to trajectory data from enhanced sampling techniques.
  • To overcome the challenge of identifying reactive trajectories requiring backward and forward time analysis.

Main Methods:

  • Introduction of simple data structures to efficiently process trajectory data.
  • Application of the method within the framework of nonequilibrium umbrella sampling.
  • Generalizable strategy applicable to various trajectory sampling methods.

Main Results:

  • Demonstrated efficient computation of transition path theory statistics from complex trajectory ensembles.
  • Successfully overcame the temporal analysis challenge in identifying reactive trajectories.
  • Provided a generalizable framework for enhanced trajectory sampling data.

Conclusions:

  • The proposed data structures offer an efficient solution for applying transition path theory to enhanced sampling data.
  • This approach broadens the applicability of transition path theory in computational chemistry and statistical mechanics.
  • The method facilitates more accurate statistical analysis of chemical reactions from complex simulations.