A one-way shooting algorithm for transition path sampling of asymmetric barriers

Brotzakis, Z. Faidon; Bolhuis, Peter G.

doi:10.1063/1.4965882

Cited by 35 publications

(56 citation statements)

References 30 publications

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“…Similar reasoning has been used before to shoot trajectories from a well chosen dividing surface 5 or near points where previous shots were successful. 8,9 In a more general form, one can choose shooting points according to a weight function w(x) in configuration (or phase) space, which would typically be defined in terms of an order parameter and adopt large values in the transition state region.…”

Section: Resultsmentioning

confidence: 99%

“…(13) and (14), to select shooting points are used. Similarly, it is straightforward to adapt the acceptance criterion to one-sided shooting 9 for dynamics with a stochastic thermostat.…”

Section: Resultsmentioning

confidence: 99%

“…Trial trajectories are shot off from the point where shooting was last successful or from points a certain number of frames before or after in time. Recently, Brotzakis and Bolhuis 9 built on the idea of biasing the choice of the starting point and extended it to one-way shooting for stochastically thermostatted dynamics. Earlier, Juraszek and Bolhuis 10 had combined transition interface sampling with a bias on the shooting points to improve the sampling efficiency for protein folding simulations.…”

Section: Introductionmentioning

confidence: 99%

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Transition path sampling of rare events by shooting from the top

Jung

Hummer

2017

The Journal of Chemical Physics

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Transition path sampling is a powerful tool in the study of rare events. Shooting trial trajectories from configurations along existing transition paths proved particularly efficient in the sampling of reactive trajectories. However, most shooting attempts tend not to result in transition paths, in particular in cases where the transition dynamics has diffusive character. To overcome the resulting efficiency problem, we developed an algorithm for "shooting from the top." We first define a shooting range through which all paths have to pass and then shoot off trial trajectories only from within this range. For a well chosen shooting range, nearly every shot is successful, resulting in an accepted transition path. To deal with multiple mechanisms, weighted shooting ranges can be used. To cope with the problem of unsuitably placed shooting ranges, we developed an algorithm that iteratively improves the location of the shooting range. The transition path sampling procedure is illustrated for models of diffusive and Langevin dynamics. The method should be particularly useful in cases where the transition paths are long so that only relatively few shots are possible, yet reasonable order parameters are known.

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Section: Resultsmentioning

confidence: 99%

“…(13) and (14), to select shooting points are used. Similarly, it is straightforward to adapt the acceptance criterion to one-sided shooting 9 for dynamics with a stochastic thermostat.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transition path sampling of rare events by shooting from the top

Jung

Hummer

2017

The Journal of Chemical Physics

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“…Most suggested variations rely on the velocity modification step and/or on the shooting point selection step. [62][63][64][65][66] However, this has as a result that two consecutive accepted paths always share some almost identical time slices. An approach to decorrelate the paths much faster was suggested in Ref.…”

Section: A New Shooting Movesmentioning

confidence: 99%

“…Direct path sampling between the unfolded and folded states can then become very inefficient as trial trajectories will get stuck in an intermediate state from which the escape itself is a rare event. While one can wait until such escapes from the intermediate state occur, even with reasonable acceptance using the stochastic one-way shooting moves 65 or the more recent spring shooting, 66 this might result in extremely long MD trajectories, thus lowering the overall path sampling efficiency dramatically. A simple solution to this problem is to conduct individual path sampling simulations for each sub-reaction.…”

Section: Multiple State Tismentioning

confidence: 99%

Foundations and latest advances in replica exchange transition interface sampling

Cabriolu

Refsnes

Bolhuis

et al. 2017

The Journal of Chemical Physics

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Nearly 20 years ago, transition path sampling (TPS) emerged as an alternative method to free energy based approaches for the study of rare events such as nucleation, protein folding, chemical reactions, and phase transitions. TPS effectively performs Monte Carlo simulations with relatively short molecular dynamics trajectories, with the advantage of not having to alter the actual potential energy surface nor the underlying physical dynamics. Although the TPS approach also introduced a methodology to compute reaction rates, this approach was for a long time considered theoretically attractive, providing the exact same results as extensively long molecular dynamics simulations, but still expensive for most relevant applications. With the increase of computer power and improvements in the algorithmic methodology, quantitative path sampling is finding applications in more and more areas of research. In particular, the transition interface sampling (TIS) and the replica exchange TIS (RETIS) algorithms have, in turn, improved the efficiency of quantitative path sampling significantly, while maintaining the exact nature of the approach. Also, open-source software packages are making these methods, for which implementation is not straightforward, now available for a wider group of users. In addition, a blooming development takes place regarding both applications and algorithmic refinements. Therefore, it is timely to explore the wide panorama of the new developments in this field. This is the aim of this article, which focuses on the most efficient exact path sampling approach, RETIS, as well as its recent applications, extensions, and variations. Published by AIP Publishing.

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Transition Path Sampling as Markov Chain Monte Carlo of Trajectories: Recent Algorithms, Software, Applications, and Future Outlook

Bolhuis

Swenson

2021

Advcd Theory and Sims

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The development of enhanced sampling methods to investigate rare but important events has always been a focal point in the molecular simulation field. Such methods often rely on prior knowledge of the reaction coordinate. However, the search for this reaction coordinate is at the heart of the rare event problem. Transition path sampling (TPS) circumvents this problem by generating an ensemble of dynamical trajectories undergoing the activated event. The reaction coordinate is extracted from the resulting path ensemble using variants of machine learning, making it an output of the method instead of an input. Over the last 20 years, since its inception, many extensions of TPS have been developed. Perhaps surprisingly, large‐scale TPS simulations on complex molecular systems have become possible only recently. Other important developments include the transition interface sampling (TIS) methodology to compute rate constants, the application to multiple states, and adaptive path sampling. The development of OpenPathSampling and PyRETIS has enabled easy and flexible use and implementation of these and other novel path sampling algorithms. In this progress report, a brief overview of recent developments, novel algorithms, and software is given. In addition, several application areas are discussed, and a future outlook for the next decade is given.

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A one-way shooting algorithm for transition path sampling of asymmetric barriers

Cited by 35 publications

References 30 publications

Transition path sampling of rare events by shooting from the top

Transition path sampling of rare events by shooting from the top

Foundations and latest advances in replica exchange transition interface sampling

Transition Path Sampling as Markov Chain Monte Carlo of Trajectories: Recent Algorithms, Software, Applications, and Future Outlook

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