Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems

DOI

Data for the publication "Performance of two complementary machine-learned potentials in modelling chemically complex systems", npj. Comp. Mat.

This data set contains

the datasets of structures in cfg and npz formats INCAR file which was used for VASP calculations python script for reading npz format

These are essentially the 2-, 3-, and 4-component configurations (converted from OUTCARs) used to train families of machine-learning potentials.

Data contains both 0K and finite-T structures of Ta-V-Cr-W subsystems, approx. 6000 configurations in total.

The "in-distribution" data has 10 splits onto training/testing parts (in 80%/20% proportion), for the cross-validation tests.

The "out-of-distribution" data is not split, it is used only for testing the accuracy.

Identifier
DOI	https://doi.org/10.18419/darus-3516
Metadata Access	https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-3516

Provenance
Creator	Gubaev, Konstantin ; Zaverkin, Viktor ; Srinivasan, Prashanth ; Duff, Andrew ; Kästner, Johannes ; Grabowski, Blazej
Publisher	DaRUS
Contributor	Gubaev, Konstantin; Blazej Grabowski
Publication Year	2023
Funding Reference	DFG KO 993 5080/3-1 ; DFG GR 3716/6-1 ; DFG 358283783 - SFB 1333
Rights	CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess	true
Contact	Gubaev, Konstantin (Universität Stuttgart); Blazej Grabowski (Universität Stuttgart)

Representation
Resource Type	Atomic configurations with energies, forces, and stresses provided by VASP; Dataset
Format	application/zip; text/plain; charset=US-ASCII; text/x-python
Size	74070343; 205818; 517; 158106134; 433097; 238
Version	1.0
Discipline	Chemistry; Engineering Sciences; Materials Science; Materials Science and Engineering; Natural Sciences; Physics