%0 Generic %1 gubaev2023performance %A Gubaev, Konstantin %A Zaverkin, Viktor %A Srinivasan, Prashanth %A Duff, Andrew %A Kästner, Johannes %A Grabowski, Blazej Tadeusz %D 2023 %K darus mult ubs_10003 ubs_20003 ubs_30032 ubs_30039 ubs_40065 ubs_40294 unibibliografie %R 10.18419/darus-3516 %T Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems %X 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 öut-of-distribution" data is not split, it is used only for testing the accuracy.

@misc{gubaev2023performance, abstract = {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. }, added-at = {2023-05-30T10:30:41.000+0200}, affiliation = {Gubaev, Konstantin/Universität Stuttgart, Zaverkin, Viktor/Universität Stuttgart, Srinivasan, Prashanth/Universität Stuttgart, Duff, Andrew/STFC Daresbury Laboratory, Kästner, Johannes/Universität Stuttgart, Grabowski, Blazej/Universität Stuttgart}, author = {Gubaev, Konstantin and Zaverkin, Viktor and Srinivasan, Prashanth and Duff, Andrew and Kästner, Johannes and Grabowski, Blazej Tadeusz}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2e5b0f24932e7e81f1ac9cfee6ba40ecf/unibiblio}, doi = {10.18419/darus-3516}, howpublished = {Dataset}, interhash = {710bb9a65a25873b3677dda461531b22}, intrahash = {e5b0f24932e7e81f1ac9cfee6ba40ecf}, keywords = {darus mult ubs_10003 ubs_20003 ubs_30032 ubs_30039 ubs_40065 ubs_40294 unibibliografie}, note = {Related to: Performance of two complementary machine-learned potentials in modelling chemically complex systems. npj. Comp. Mat}, orcid-numbers = {Gubaev, Konstantin/0000-0003-2612-8515, Zaverkin, Viktor/0000-0001-9940-8548, Srinivasan, Prashanth/0000-0002-9199-4340, Duff, Andrew/0000-0002-5073-4112, Kästner, Johannes/0000-0001-6178-7669, Grabowski, Blazej/0000-0003-4281-5665}, timestamp = {2023-05-30T10:30:41.000+0200}, title = {Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems}, year = 2023 }