<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Drigo, Enrico</dc:creator> <dc:creator>Baroni, Stefano</dc:creator> <dc:creator>Pegolo, Paolo</dc:creator> <dc:date>2024-05-13</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2024.71</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:p1-bm</dc:identifier> <dc:identifier>mcid:2024.71</dc:identifier> <dc:identifier>oai:materialscloud.org:2181</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Seebeck</dc:subject> <dc:subject>Bayesian</dc:subject> <dc:subject>ionic conductors</dc:subject> <dc:title>Seebeck coefficient of ionic conductors from Bayesian regression analysis</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>