Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

DOI

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).

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.

Identifier
DOI https://doi.org/10.14278/rodare.646
Related Identifier https://arxiv.org/abs/2010.04905
Related Identifier https://www.hzdr.de/publications/Publ-31857
Related Identifier https://www.hzdr.de/publications/Publ-31603
Related Identifier https://doi.org/10.14278/rodare.645
Related Identifier https://rodare.hzdr.de/communities/hzdr
Related Identifier https://rodare.hzdr.de/communities/matter
Related Identifier https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:646
Provenance
Creator Ellis, J. A.; Cangi, A.; Modine, N. A.; Stephens, J. A.; Thompson, A. P.; Rajamanickam, S.
Publisher Rodare
Publication Year 2020
Rights Restricted Access; info:eu-repo/semantics/restrictedAccess
OpenAccess false
Contact https://rodare.hzdr.de/support
Representation
Resource Type Dataset
Discipline Engineering Sciences; Materials Science; Materials Science and Engineering