Data for: Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten

DOI

The dataset contains key files to reproduce the results presented in the article " Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten":

DFT input files: INCAR, KPOINTS. All POSCAR files for DFT and thermodynamic integration Moment tensor potential (MTP) file Training dataset for MTP All Hessian Matrix files for thermodynamic integration. For the transition state, the stabilized Hessian Matrix is provided. All imaginary mode files for transition state Lattice expansion used in all calculations.

Identifier
DOI https://doi.org/10.18419/DARUS-4564
Related Identifier IsSupplementTo https://doi.org/10.1038/s41467-024-55759-w
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-4564
Provenance
Creator Zhang, Xi ORCID logo
Publisher DaRUS
Contributor Zhang, Xi; Blazej Grabowski
Publication Year 2024
Funding Reference European Commission info:eu-repo/grantAgreement/EC/H2020/865855 ; DFG 509804947
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Zhang, Xi (Universität Stuttgart); Blazej Grabowski (Universität Stuttgart)
Representation
Resource Type Dataset
Format application/zip; text/x-fixed-field; text/plain; charset=US-ASCII; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Size 2435; 13766965; 33313; 102; 762902; 34261072; 27046; 105143562
Version 2.0
Discipline Construction Engineering and Architecture; Engineering; Engineering Sciences; Natural Sciences; Physics