Data for: Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions

DOI

Data for reproducing the results in the manuscript "Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions". Find further details in README.md.

MOLPRO, 2024.1

VASP, 6

MLIP, 3

Identifier
DOI https://doi.org/10.18419/DARUS-5272
Related Identifier IsSupplementTo https://doi.org/10.1021/acs.jctc.5c02045
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5272
Provenance
Creator Ikeda, Yuji ORCID logo; Forslund, Axel ORCID logo; Kumar, Pranav ORCID logo; Ou, Yongliang ORCID logo; Jung, Jong Hyun (ORCID: 0000-0002-2409-975X); Koehn, Andreas (ORCID: 0000-0002-0844-842X); Grabowski, Blazej ORCID logo
Publisher DaRUS
Contributor Grabowski, Blazej
Publication Year 2026
Funding Reference DFG 358283783 ; DFG 390740016 ; DFG 519607530 ; DFG 405998092 ; European Commission info:eu-repo/grantAgreement/EC/H2020/865855
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Grabowski, Blazej (University of Stuttgart)
Representation
Resource Type Dataset
Format application/zip; text/markdown
Size 5617341; 49761457; 4513569; 31668; 2291
Version 1.0
Discipline Chemistry; Construction Engineering and Architecture; Design; Engineering; Engineering Sciences; Fine Arts, Music, Theatre and Media Studies; Humanities; Natural Sciences; Physics