MLIPs Ontology

DOI

MLIPs Ontology: An Ontology for Machine Learning Interatomic Potentials

More information can be found in the README.md.

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Identifier
DOI https://doi.org/10.18419/DARUS-5948
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5948
Provenance
Creator Hernández, Daniel ORCID logo; Jung, Jong Hyun (ORCID: 0000-0002-2409-975X); Ikeda, Yuji ORCID logo; Ou, Yongliang ORCID logo; Kumar, Pranav ORCID logo; Tom Schächtel ORCID logo; Liu, Wenchuan ORCID logo; Li, Xin ORCID logo; Zhang, Xi ORCID logo; Xu, Xiang ORCID logo; Zhu, Li-Fang ORCID logo; Körmann, Fritz ORCID logo; Staab, Steffen ORCID logo; Grabowski, Blazej ORCID logo
Publisher DaRUS
Contributor Hernández, Daniel
Publication Year 2026
Funding Reference European Commission info:eu-repo/grantAgreement/EC/HE/101200433
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Hernández, Daniel (University of Stuttgart)
Representation
Resource Type Dataset
Format application/octet-stream; application/json; text/plain; charset=US-ASCII; text/markdown
Size 3853; 5379; 18657; 1027488; 5323
Version 1.0
Discipline Chemistry; Computer Science; Computer Science, Electrical and System Engineering; Condensed Matter Physics; Engineering Sciences; Materials Science; Materials Science and Engineering; Natural Sciences; Physics