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Area of Science:

  • Computational chemistry
  • Biophysics
  • Statistical mechanics

Background:

  • Studying rare events and transition pathways in complex systems is challenging.
  • Existing methods often rely on approximations limiting their applicability.

Purpose of the Study:

  • To develop a novel neural network-based strategy for discovering physically meaningful and committor-consistent transition pathways.
  • To provide an unprecedented view of transition processes in complex systems.

Main Methods:

  • A neural network strategy learning the committor function and its associated committor-consistent string simultaneously.
  • Utilizing the committor time-correlation function across diverse dynamical regimes.
  • Extending beyond traditional infinitesimal time-lag approximations.

Main Results:

  • The method successfully reproduces established dynamics, rate constants, and transition mechanisms.
  • It distinguishes multiple competing pathways in complex biomolecular transformations.
  • Demonstrated robustness on benchmark potentials and biological systems like peptide isomerization and protein folding.

Conclusions:

  • This approach offers a powerful and versatile tool for enhanced-sampling simulations of rare events.
  • It provides valuable insights into the intricate landscapes of biomolecular systems.
  • The method is adaptable to collective variables and resilient across neural architectures.