2020
DOI: 10.1088/1742-5468/ab5368
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Effective equations for reaction coordinates in polymer transport

Abstract: In the framework of the problem of finding proper reaction coordinates (RCs) for complex systems and their effective evolution equations, we consider the case study of a polymer chain in an external double-well potential, experiencing thermally activated dynamics. Langevin effective equations describing the macroscopic dynamics of the system can be inferred from data by using a data-driven approach, once a suitable set of RCs is chosen.We show that, in this case, the validity of such choice depends on the stif… Show more

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Cited by 4 publications
(2 citation statements)
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“…If this was the case, under the assumption that v 2 − v 2 x 2 − x 2 (i.e. the dynamics is close to the "continuous limit"), one could exploit a data-driven approach to derive the form of an approximate discrete-time Langevin equation 18,21,22,50 ; this strategy is based on the possibility to infer an approximate functional form for f (x, v) by considering the small-time limit of suitable conditioned moments. Unfortunately, the model inferred with this method (not shown) is not able to reproduce the original trajectory, a clear hint that Eq.…”
Section: A Validation Methodsmentioning
confidence: 99%
“…If this was the case, under the assumption that v 2 − v 2 x 2 − x 2 (i.e. the dynamics is close to the "continuous limit"), one could exploit a data-driven approach to derive the form of an approximate discrete-time Langevin equation 18,21,22,50 ; this strategy is based on the possibility to infer an approximate functional form for f (x, v) by considering the small-time limit of suitable conditioned moments. Unfortunately, the model inferred with this method (not shown) is not able to reproduce the original trajectory, a clear hint that Eq.…”
Section: A Validation Methodsmentioning
confidence: 99%
“…A number of algorithms aim at parametrizing Langevin models starting from trajectories of many-particle systems. ,, More in detail, non-Markovian friction (the so-called memory kernel) can be reconstructed in different ways, for instance based on a set of time-correlation functions and Volterra integral equations (see, e.g., refs , , and ) or via likelihood maximization . In the case of Markovian (overdamped) Langevin equations, the latter approach, ,, together with direct estimation of Kramers-Moyal coefficients, , has been employed.…”
Section: Introductionmentioning
confidence: 99%