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The trajectory of a molecular system undergoing a reversible reaction A ⇌ B and crossing and recrossing a transition state separating the reactant and product consists of loops, i.e., excursions from the transition state to either side and back to the transition state. Motivated by recent experimental observations of loops, here, we discuss some of their statistical properties. In particular, we highlight that the transition-state rate is not only an upper bound on the true reaction rate but also a physical property of the loops. We further find that loops can be unambiguously divided into two sub-ensembles. Those consist of short loops, which are brief excursions from the transition state, and long loops that get trapped in the reactant or product wells before eventually returning to the barrier. Finally, we show that the loop time distribution contains information about both the reaction rate coefficients and their transition-state-theory counterparts.
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This content will become publicly available on April 8, 2026
Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories
This study presents a transition path sampling (TPS) procedure to create an ensemble of trajectories describing a chemical transformation from a reactant to a product state, augmented with a computer vision technique. A 3D convolutional neural network (CNN) sorts the slices of the TPS trajectories into reactant or product state categories, which aids in automatically accepting or rejecting a newly generated trajectory. Furthermore, information about the geometrical configuration of each slice enables one to calculate the percentage of reactant and product states within a specific shooting range. These statistics are used to determine the most appropriate shooting range and, if needed, to improve a shooting acceptance rate. To test the automated 3D CNN TPS technique, we applied it to collect an ensemble of the transition paths for the rate-limiting step of the Morita−Bayliss−Hillman (MBH) reaction.
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- Award ID(s):
- 2244981
- PAR ID:
- 10651535
- Publisher / Repository:
- ACS JCTC
- Date Published:
- Journal Name:
- Journal of Chemical Theory and Computation
- Volume:
- 21
- Issue:
- 7
- ISSN:
- 1549-9618
- Page Range / eLocation ID:
- 3353 to 3359
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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