Seebeck coefficient of ionic conductors from Bayesian regression analysis

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...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Drigo, Enrico, Baroni, Stefano, Pegolo, Paolo
Format: Article
Language:English
Subjects:
Bayesian analysis
Cesium fluorides
Electrolytes
Empirical analysis
Molecular dynamics
Neural networks
Regression analysis
Seebeck effect
Statistical analysis
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Summary: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. To validate the effectiveness of our method, we benchmark it against extensive equilibrium molecular dynamics simulations conducted on molten \(\mathrm{CsF}\) using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten \(\mathrm{NaCl}\), \(\mathrm{KCl}\) and \(\mathrm{LiCl}\) using neural-network force fields trained on ab initio data over a range of pressure-temperature conditions.
ISSN:2331-8422