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Following the Committor Flow: A Data-Driven Discovery of Transition Pathways.

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This study introduces a novel iterative framework to accurately identify molecular transition pathways and estimate reaction rates. The method refines the committor probability using neural networks for enhanced molecular dynamics simulations.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics
  • Reaction Mechanism Discovery

Background:

  • Identifying rare events and transition pathways in molecular systems remains a significant challenge.
  • The committor probability is a key metric for defining reaction coordinates in molecular simulations.
  • Ensuring consistency between transition pathways and the committor function is crucial for mechanistic accuracy.

Purpose of the Study:

  • To develop an iterative framework for inferring the committor probability.
  • To identify and refine the most relevant transition pathways in molecular systems.
  • To enable accurate estimation of reaction rate constants.

Main Methods:

  • An iterative approach using biased sampling and neural network approximation of the committor probability.
  • Extraction of dominant transition channels from learned committor isocommittor surfaces.
  • Iterative refinement of committor and transition paths until convergence.

Main Results:

  • The proposed framework successfully infers the committor probability and identifies key transition pathways.
  • Demonstrated effectiveness on benchmark systems: 2D model potential, peptide transitions, Diels-Alder reaction, and Trp-cage folding.
  • The refined committor enables accurate reaction rate constant estimation.

Conclusions:

  • The iterative framework provides a robust method for discovering transition pathways and understanding reaction mechanisms.
  • This approach enhances molecular dynamics simulations by improving the accuracy of committor estimation.
  • The method offers a significant advancement in the study of rare events in molecular systems.