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

Dataset

DOI

The dataset contains all necessary 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, POTCAR. 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 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
Metadata Access	https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-4564

Provenance
Creator	Zhang, Xi
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	84947; 13766965; 33313; 102; 762902; 34261072; 27046; 105143562
Version	1.0
Discipline	Construction Engineering and Architecture; Engineering; Engineering Sciences; Natural Sciences; Physics