Name: CoNbV_CMS2019 Extended ID: CoNbV_CMS2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_sn623uhg2d1b_0 Description: This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included about 27,000 configurations that were bcc-like and close-packed (fcc, hcp, etc.) with 8 or fewer atoms in the unit cell and different concentrations of Co, Nb, and V. Authors: Konstantin Gubaev Evgeny V. Podryabinkin Gus L.W. Hart Alexander V. Shapeev DOI: 10.60732/f2c623f1 Calculated Property Types: atomic_forces cauchy_stress energy Elements: Co (54.77%) Nb (26.24%) V (18.99%) Methods: DFT-undefined Software: VASP Number of Configurations: 383 Number of Atoms: 2,812 Publication Link: https://doi.org/10.1016/j.commatsci.2018.09.031 Data Source Link: https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data