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Seebeck coefficient of ionic conductors from Bayesian regression analysis

Enrico Drigo1*, Stefano Baroni1, Paolo Pegolo2

1 SISSA – Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy

2 COSMO, Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland

* Corresponding authors emails: endrigo@sissa.it
DOI10.24435/materialscloud:p1-bm [version v1]

Publication date: May 13, 2024

How to cite this record

Enrico Drigo, Stefano Baroni, Paolo Pegolo, Seebeck coefficient of ionic conductors from Bayesian regression analysis, Materials Cloud Archive 2024.71 (2024), https://doi.org/10.24435/materialscloud:p1-bm


We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient, whose statistical uncertainty can be arbitrarily reduced in the long-time limit. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions.

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Seebeck Bayesian ionic conductors

Version history:

2024.71 (version v1) [This version] May 13, 2024 DOI10.24435/materialscloud:p1-bm