ArticleThe Journal of chemical physics2022
Computing transition path theory quantities with trajectory stratification.
Article in The Journal of chemical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- CHARMM-GUIJournal of chemical theory and computation · 2025Article
- Analysis of the Dynamics of a Complex, Multipathway Reaction: Insulin Dimer Dissociation.The journal of physical chemistry. B · 2024Article
- BAD-NEUS: Rapidly converging trajectory stratification.The Journal of chemical physics · 2024Article
- Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE.Journal of chemical information and modeling · 2024Article
- Dynamics of activation in the voltage-sensing domain of Ciona intestinalis phosphatase Ci-VSP.Nature communications · 2024Article
- Article
- AlphaFold2-RAVE: From Sequence to Boltzmann Ranking.Journal of chemical theory and computation · 2023Article
- Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction.The Journal of chemical physics · 2023Article
- Augmented transition path theory for sequences of events.The Journal of chemical physics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Transition path theory computes statistics from ensembles of reactive trajectories. A common strategy for sampling reactive trajectories is to control the branching and pruning of trajectories so as to enhance the sampling of low probability segments. However, it can be challenging to apply transition path theory to data from such methods because determining whether configurations and trajectory segments are part of reactive trajectories requires looking backward and forward in time. Here, we show how this issue can be overcome efficiently by introducing simple data structures. We illustrate the approach in the context of nonequilibrium umbrella sampling, but the strategy is general and can be used to obtain transition path theory statistics from other methods that sample segments of unbiased trajectories.
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Registered trials
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