2021
DOI: 10.48550/arxiv.2110.05050
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Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain

Dario Lucente,
Joran Rolland,
Corentin Herbert
et al.

Abstract: Rare events play a crucial role in many physics, chemistry, and biology phenomena, when they change the structure of the system, for instance in the case of multistability, or when they have a huge impact. Rare event algorithms have been devised to simulate them efficiently, avoiding the computation of long periods of typical fluctuations. We consider here the family of splitting or cloning algorithms, which are versatile and specifically suited for far-from-equilibrium dynamics. To be efficient, these algorit… Show more

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“…In the machine-learning context, one can indeed try to compute the committor function in some reduced space of collective variables [3], (see also [38]). A more involved approach denoted loop-AMS has been proposed recently which amounts to identify a better reaction coordinate in a dynamical way [44]. Although the results are encouraging, they also face the issue of the small noiseamplitude limit: the committor function in phase space becomes nearly singular with Heaviside-type of behavior.…”
Section: Other Approaches: Rare Event Algorithmsmentioning
confidence: 99%
“…In the machine-learning context, one can indeed try to compute the committor function in some reduced space of collective variables [3], (see also [38]). A more involved approach denoted loop-AMS has been proposed recently which amounts to identify a better reaction coordinate in a dynamical way [44]. Although the results are encouraging, they also face the issue of the small noiseamplitude limit: the committor function in phase space becomes nearly singular with Heaviside-type of behavior.…”
Section: Other Approaches: Rare Event Algorithmsmentioning
confidence: 99%