Dataset Restricted Access
Ellis, J. A.; Cangi, A.; Modine, N. A.; Stephens, J. A.; Thompson, A. P.; Rajamanickam, S.
<?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>Ellis, J. A.</dc:creator> <dc:creator>Cangi, A.</dc:creator> <dc:creator>Modine, N. A.</dc:creator> <dc:creator>Stephens, J. A.</dc:creator> <dc:creator>Thompson, A. P.</dc:creator> <dc:creator>Rajamanickam, S.</dc:creator> <dc:date>2020-12-11</dc:date> <dc:description>Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication (https://arxiv.org/abs/2010.04905).</dc:description> <dc:description>This is only a limited set of the entire output data. The remainder of the data will be made available at a later point once approval from the collaborating research institution (Sandia National Laboratories) has been granted. The source code of the associated machine learning framework will also be published at that stage.</dc:description> <dc:identifier>https://rodare.hzdr.de/record/646</dc:identifier> <dc:identifier>10.14278/rodare.646</dc:identifier> <dc:identifier>oai:rodare.hzdr.de:646</dc:identifier> <dc:relation>url:https://arxiv.org/abs/2010.04905</dc:relation> <dc:relation>url:https://www.hzdr.de/publications/Publ-31857</dc:relation> <dc:relation>url:https://www.hzdr.de/publications/Publ-31603</dc:relation> <dc:relation>doi:10.14278/rodare.645</dc:relation> <dc:relation>url:https://rodare.hzdr.de/communities/hzdr</dc:relation> <dc:relation>url:https://rodare.hzdr.de/communities/matter</dc:relation> <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation> <dc:rights>info:eu-repo/semantics/restrictedAccess</dc:rights> <dc:subject>machine learning</dc:subject> <dc:subject>neural networks</dc:subject> <dc:subject>materials science</dc:subject> <dc:subject>density functional theory</dc:subject> <dc:title>Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks</dc:title> <dc:type>info:eu-repo/semantics/other</dc:type> <dc:type>dataset</dc:type> </oai_dc:dc>
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