<?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>Bussy, Augustin</dc:creator> <dc:creator>Hutter, Jürg</dc:creator> <dc:date>2021-07-29</dc:date> <dc:description>A new implementation of linear-response time-dependent density functional theory (LR-TDDFT) for core level near-edge absorption spectroscopy is discussed. The method is based on established LR-TDDFT approaches to X-ray absorption spectroscopy (XAS) with additional accurate approximations for increased efficiency. We validate our implementation by reproducing benchmark results at the K-edge and showing that spin–orbit coupling effects at the L2,3-edge are well described. We also demonstrate that the method is suitable for extended systems in periodic boundary conditions and measure a favorable sub-cubic scaling of the calculation cost with system size. We finally show that GPUs can be efficiently exploited and report speedups of up to a factor 2.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2021.125</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:js-me</dc:identifier> <dc:identifier>mcid:2021.125</dc:identifier> <dc:identifier>oai:materialscloud.org:970</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>MARVEL/DD4</dc:subject> <dc:subject>TDDFT</dc:subject> <dc:subject>XAS</dc:subject> <dc:subject>Method development</dc:subject> <dc:subject>Low-scaling algorithm</dc:subject> <dc:title>Efficient and low-scaling linear-response time-dependent density functional theory implementation for core-level spectroscopy of large and periodic systems</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>