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  4. SporTran: A code to estimate transport coefficients from the cepstral analysis of (multivariate) current time series
 
research article

SporTran: A code to estimate transport coefficients from the cepstral analysis of (multivariate) current time series

Ercole, Loris  
•
Bertossa, Riccardo
•
Bisacchi, Sebastiano
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November 1, 2022
Computer Physics Communications

SporTran is a Python utility designed to estimate generic transport coefficients in extended systems, based on the Green-Kubo theory of linear response and the recently introduced cepstral analysis of the current time series generated by molecular dynamics simulations. SporTran can be applied to univariate as well as multivariate time series. Cepstral analysis requires minimum discretion from the user, in that it weakly depends on two parameters, one of which is automatically estimated by a statistical model-selection criterion that univocally determines the resulting accuracy. In order to facilitate the optimal refinement of these parameters, SporTran features an easy-to-use graphical user interface. A command-line interface and a Python API, easy to embed in complex data-analysis workflows, are also provided. (C) 2022 Elsevier B.V. All rights reserved.

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Type
research article
DOI
10.1016/j.cpc.2022.108470
Web of Science ID

WOS:000863271300003

Author(s)
Ercole, Loris  
Bertossa, Riccardo
Bisacchi, Sebastiano
Baroni, Stefano
Date Issued

2022-11-01

Publisher

ELSEVIER

Published in
Computer Physics Communications
Volume

280

Article Number

108470

Subjects

Computer Science, Interdisciplinary Applications

•

Physics, Mathematical

•

Computer Science

•

Physics

•

molecular dynamics

•

transport coefficients

•

green kubo

•

linear response

•

time series

•

cepstral analysis

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statistical-mechanical theory

•

markov random-processes

•

irreversible-processes

•

reciprocal relations

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
THEOS  
Available on Infoscience
October 24, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/191586
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