<?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>Izzo, Maria Grazia</dc:creator> <dc:creator>Baroni, Stefano</dc:creator> <dc:date>2023-10-26</dc:date> <dc:description>We present a method, based on the classical Green-Kubo theory of linear response, to compute the heat conductivity of extended systems, leveraging energy-density, rather than energy-current, fluctuations, thus avoiding the need to devise an analytical expression for the macroscopic energy flux. The implementation of this method requires the evaluation of the long-wavelength and low-frequency limits of a suitably defined correlation function, which we perform using a combination of recently-introduced cepstral-analysis and Bayesian extrapolation techniques. Our methodology is demonstrated against standard current-based Green-Kubo results for liquid argon and water, and solid amorphous Silica, and compared with a recently proposed similar technique, which utilizes mass-density, instead of energy-density, fluctuations.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2023.162</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:ft-b3</dc:identifier> <dc:identifier>mcid:2023.162</dc:identifier> <dc:identifier>oai:materialscloud.org:1950</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>thermal transport</dc:subject> <dc:subject>heat conductivity</dc:subject> <dc:subject>equilibrium molecular dynamics</dc:subject> <dc:subject>energy density</dc:subject> <dc:title>Heat conductivity from energy-density fluctuations</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>