New method uses Koopman operators for rare transition sampling

Researchers have developed a novel approach to transition path sampling (TPS) in dynamical systems, particularly for molecular dynamics, by leveraging Koopman operators and exit-time optimal control. This method addresses the challenge of rare transitions across high free-energy barriers. By reformulating TPS as an optimal stochastic control problem with a time horizon defined by the first hitting time of a target set, and by using the committor function to penalize time spent in nonreactive regions, the approach derives an optimal controller. This controller, approximated in a reproducing kernel Hilbert space, significantly increases the fraction of trajectories reaching the target set, demonstrating effectiveness on benchmark systems like the two-channel double well and alanine dipeptide. AI

IMPACT This research introduces a novel computational method for simulating rare events in complex systems, potentially accelerating discovery in fields like molecular dynamics and materials science.

RANK_REASON Academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]

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• Alanine Dipeptide
• artificial neural network
• Exit-Time Optimal Control
• Karush-Kuhn-Tucker system
• Koopman Operators
• Optimal Stochastic Control of Artificial Pancreas under Model Uncertainties
• reproducing kernel Hilbert space
• Transition path sampling

• paper
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