Dataset
CoNbV_CMS2019
Download Dataset XYZ file
Name | CoNbV_CMS2019 |
---|---|
Extended ID | CoNbV_CMS2019_GubaevPodryabinkinHartShapeev__DS_visyvn84h78m_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_visyvn84h78m_0 |
Files | colabfitspec.json |
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