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

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.