Dataset

CoNbV_CMS2019



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Name CoNbV_CMS2019
Extended ID CoNbV_CMS2019_GubaevPodryabinkinHartShapeev__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
Elements Co (54.77%)
Nb (26.24%)
V (18.99%)
Number of Data Objects 383
Number of Configurations 383
Number of Atoms 2,812
Links https://doi.org/10.1016/j.commatsci.2018.09.031
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_sn623uhg2d1b_0
Files colabfitspec.json

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